From 213b63a2eaffdc831998ee9d10a2642c0c1f8e84 Mon Sep 17 00:00:00 2001 From: yongzhuo <2714618994@qq.com> Date: Wed, 1 Jan 2020 00:51:11 +0800 Subject: [PATCH] macropodus init --- README.md | 238 +- __init__.py | 5 + macropodus/__init__.py | 50 + macropodus/__init_tf_keras.py | 54 + macropodus/base/__init__.py | 5 + macropodus/base/seg_basic.py | 148 + macropodus/base/word2vec.py | 64 + macropodus/conf/__init__.py | 5 + macropodus/conf/path_config.py | 38 + macropodus/conf/path_log.py | 50 + macropodus/data/__init__.py | 5 + macropodus/data/cache/__init__.py | 5 + macropodus/data/dict/macropodus.dict | 1 + macropodus/data/dict/user.dict | 1 + .../data/embedding/albert_base_zh/__init__.py | 5 + macropodus/data/embedding/term_char.txt | 15515 ++++++++++ .../word2vec/w2v_model_wiki_char.vec | 24382 ++++++++++++++++ macropodus/data/model/__init__.py | 5 + macropodus/data/words_common/__init__.py | 5 + macropodus/data/words_common/stop_words.py | 125 + macropodus/logs/__init__.py | 5 + 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macropodus_survey_report/nlp_platfom_survey.md create mode 100644 requirements.txt create mode 100644 setup.py create mode 100644 test/evaluate/__init__.py create mode 100644 test/evaluate/data/__init__.py create mode 100644 test/evaluate/data/ambiguity.txt create mode 100644 test/evaluate/tet_evaluate.py create mode 100644 test/evaluate/tet_macropodus.py diff --git a/README.md b/README.md index 49ebe52..7eaed7c 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,236 @@ -# Macropodus -Macropodus自然语言处理工具(Albert+BiLSTM+CRF) 中文分词 命名实体识别 新词发现 关键词 文本摘要 计算器 中文数字阿拉伯数字转换 +

+ +

+ +# [Macropodus](https://github.com/yongzhuo/Macropodus) + +[![PyPI](https://img.shields.io/pypi/v/Macropodus)](https://pypi.org/project/Macropodus/) +[![Build Status](https://travis-ci.com/yongzhuo/Macropodus.svg?branch=master)](https://travis-ci.com/yongzhuo/Macropodus) +[![PyPI_downloads](https://img.shields.io/pypi/dm/Macropodus)](https://pypi.org/project/Macropodus/) +[![Stars](https://img.shields.io/github/stars/yongzhuo/Macropodus?style=social)](https://github.com/yongzhuo/Macropodus/stargazers) +[![Forks](https://img.shields.io/github/forks/yongzhuo/Macropodus.svg?style=social)](https://github.com/yongzhuo/Macropodus/network/members) +[![Join the chat at https://gitter.im/yongzhuo/Macropodus](https://badges.gitter.im/yongzhuo/Macropodus.svg)](https://gitter.im/yongzhuo/Macropodus?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +>>> Macropodus是一个以Albert+BiLSTM+CRF网络结构为基础，用大规模中文语料训练的自然语言处理工具包。将提供中文分词、命名实体识别、关键词抽取、文本摘要、新词发现、文本相似度、计算器、数字转化等常见NLP功能。 + + +## 目录 + +* [安装](#安装) +* [使用方式](#使用方式) +* [参考/引用](#参考/引用) +* [FAQ](#FAQ) + + +# 安装 +1. 通过PyPI安装(自带模型文件)： + ``` + pip install macropodus + ``` +2. 使用镜像源，例如： + ``` + pip install -i https://pypi.tuna.tsinghua.edu.cn/simple macropodus + ``` + + +# 使用方式 + + +## 快速使用 +```python3 +import macropodus + +sen_calculate = "23 + 13 * (25+(-9-2-5-2*3-6/3-40*4/(2-3)/5+6*3))加根号144你算得几多" +sen_chi2num = "三千零七十八亿三千零十五万零三百一十二点一九九四" +sen_num2chi = 1994.1994 +sent1 = "PageRank算法简介" +sent2 = "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" +summary = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + +# 分词(词典最大概率分词DAG) +words = macropodus.cut(summary) +print(words) +# 新词发现 +new_words = macropodus.find(summary) +print(new_words) +# 文本摘要 +sum = macropodus.summarize(summary) +print(sum) +# 关键词抽取 +keyword = macropodus.keyword(summary) +print(keyword) +# 文本相似度 +sim = macropodus.sim(sent1, sent2) +print(sim) +# tookit +# 计算器 +score_calcul = macropodus.calculate(sen_calculate) +print(score_calcul) +# 中文数字与阿拉伯数字相互转化 +res_chi2num = macropodus.chi2num(sen_chi2num) +print(res_chi2num) +res_num2chi = macropodus.num2chi(sen_num2chi) +print(res_num2chi) + +``` + + +## 中文分词 + +各种分词方法 +```python3 +import macropodus + +# 用户词典 +macropodus.add_word(word="斗鱼科") +macropodus.add_word(word="鲈形目") # 不持久化, 当前有效 +macropodus.save_add_words(word_freqs={"喜斗":32, "护卵":64, "护幼":132}) # 持久化保存到用户字典 +sent = "斗鱼属，Macropodus (1801)，鲈形目斗鱼科的一属鱼类。本属鱼类通称斗鱼。因喜斗而得名。分布于亚洲东南部。中国有2种，即叉尾斗鱼，分布于长江及以南各省；叉尾斗鱼，分布于辽河到珠江流域。其喜栖居于小溪、河沟、池塘、稻田等缓流或静水中。雄鱼好斗，产卵期集草成巢，雄鱼口吐粘液泡沫，雌鱼产卵其中，卵浮性，受精卵在泡沫内孵化。雄鱼尚有护卵和护幼现象。" + +# 分词 +sents = macropodus.cut_bidirectional(sent) +print("cut_bidirectional: " + " ".join(sents)) +sents = macropodus.cut_forward(sent) +print("cut_forward: " + " ".join(sents)) +sents = macropodus.cut_reverse(sent) +print("cut_reverse: " + " ".join(sents)) +sents = macropodus.cut_search(sent) +print("cut_search: " + " ".join(sents)) +# DAG +sents = macropodus.cut_dag(sent) +print("cut_dag: " + " ".join(sents)) + +``` + + +## 文本相似度 + + 文本相似度主要使用词向量, 余弦相似度 或 jaccard相似度 +```python3 +import macropodus + +sent1="叉尾斗鱼是一种观赏性动物" +sent2="中国斗鱼生性好斗,适应性强,能在恶劣的环境中生存" + +# 文本相似度(similarity) +sents = macropodus.sim(sent1, sent2, type_sim="total", type_encode="avg") +print(sents) +sents = macropodus.sim(sent1, sent2, type_sim="cosine", type_encode="single") +print(sents) + +``` + + +## 文本摘要 + + 文本摘要方法有text_pronouns, text_teaser, word_sign, textrank, lead3, mmr, lda, lsi, nmf +```python3 +import macropodus + +summary = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + +# 文本摘要(summarize, 默认接口) +sents = macropodus.summarize(summary) +print(sents) + +# 文本摘要(summarization, 可定义方法, 提供9种文本摘要方法, 'lda', 'mmr', 'textrank', 'text_teaser') +sents = macropodus.summarization(text=summary, type_summarize="lda") +print(sents) + +``` + + +## 新词发现 + + 新词发现主要使用凝固度, 左熵, 右熵, 词频等方案, 综合考虑 +```python3 +import macropodus + +summary = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + +# 新词发现(findword, 默认接口) +sents = macropodus.find(text=summary, freq_min=2, len_max=7, entropy_min=1.2, aggregation_min=0.5, use_avg=True) +print(sents) + +``` + + +## 关键词 + + 关键词抽取使用的是textrank, 边关系构建: 1. 字向量构建句向量; 2. 余弦相似度计算边得分 +```python3 +import macropodus + +sent = "斗鱼属，Macropodus (1801)，鲈形目斗鱼科的一属鱼类。本属鱼类通称斗鱼。因喜斗而得名。分布于亚洲东南部。中国有2种，即叉尾斗鱼，分布于长江及以南各省；叉尾斗鱼，分布于辽河到珠江流域。其喜栖居于小溪、河沟、池塘、稻田等缓流或静水中。雄鱼好斗，产卵期集草成巢，雄鱼口吐粘液泡沫，雌鱼产卵其中，卵浮性，受精卵在泡沫内孵化。雄鱼尚有护卵和护幼现象。" +# 关键词(keyword) +sents = macropodus.keyword(sent) +print(sents) + +``` + + +## 常用小工具(tookit) + + 工具包括科学计算器, 阿拉伯-中文数字转化 +```python3 +import macropodus + +sen_calculate = "23 + 13 * (25+(-9-2-5-2*3-6/3-40*4/(2-3)/5+6*3))加根号144你算得几多" +sen_chi2num = "三千零七十八亿三千零十五万零三百一十二点一九九四" +sen_num2chi = 1994.1994 +# tookit, 科学计算器 +score_calcul = macropodus.calculate(sen_calculate) +print(score_calcul) +# tookit, 中文数字转阿拉伯 +res_chi2num = macropodus.chi2num(sen_chi2num) +print(res_chi2num) +# tookit, 阿拉伯数字转中文 +res_num2chi = macropodus.num2chi(sen_num2chi) +print(res_num2chi) + +``` + +# 参考/引用 +* textrank_gensim: [https://github.com/RaRe-Technologies/gensim](https://github.com/RaRe-Technologies/gensim) +* 最大概率(DAG-动态规划)词典分词: [https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba) +* CRF(-未解决): [https://github.com/BrikerMan/Kashgari](https://github.com/BrikerMan/Kashgari) + +# FAQ diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..de268ba --- /dev/null +++ b/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/3 22:50 +# @author :Mo +# @function : diff --git a/macropodus/__init__.py b/macropodus/__init__.py new file mode 100644 index 0000000..8033e36 --- /dev/null +++ b/macropodus/__init__.py @@ -0,0 +1,50 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/12 22:57 +# @author : Mo +# @function: init of macropodus (tookit, keras of tensorflow) + + +# macropodus +from macropodus.segment import cut_bidirectional, cut_forward, cut_reverse, cut_search, cut_dag, cut, find +from macropodus.segment import load_user_dict, save_delete_words, save_add_words, delete_word, add_word +from macropodus.summarize import keyword, textrank, summarization +from macropodus.tookit import calculate, chi2num, num2chi, Trie +from macropodus.version import __version__ # 版本 +from macropodus.similarity import sim + +# 机械分词 +cut_bidirectional = cut_bidirectional +cut_forward = cut_forward +cut_reverse = cut_reverse +cut_search = cut_search +cut_dag = cut_dag +cut = cut + +# 用户词典操作 +load_user_dict = load_user_dict +save_delete_words = save_delete_words # 保存到用户词典的 +save_add_words = save_add_words +delete_word = delete_word +add_word = add_word + +# 新词发现 +find = find + +# 文本相似度 +sim = sim + +# 文本摘要, 关键词 +keyword = keyword +summarize = textrank +summarization = summarization + +# 常用工具(tookit, 计算器, 中文与阿拉伯数字转化, 前缀树) +calculate = calculate +chi2num = chi2num +num2chi = num2chi + +# 是否使用深度学习模型 +use_dl=False +if use_dl: + from macropodus.__init_tf_keras import * # tf.python.keras, custom_objects diff --git a/macropodus/__init_tf_keras.py b/macropodus/__init_tf_keras.py new file mode 100644 index 0000000..047524d --- /dev/null +++ b/macropodus/__init_tf_keras.py @@ -0,0 +1,54 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 22:22 +# @author : Mo +# @function: init of keras of tensorflow + + +#####################(tensorflow, keras)############################ +import sys +import os +path_root = os.path.abspath(os.path.dirname(__file__)) +sys.path.append(path_root) # 环境引入根目录 +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +os.environ['TF_KERAS'] = '1' + +try: + # tensorflow.python.keras + import tensorflow.python.keras as keras + import tensorflow as tf + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +except: + import keras + +# custom_objects +import keras_bert +custom_objects = keras_bert.get_custom_objects() +from keras_adaptive_softmax import AdaptiveEmbedding, AdaptiveSoftmax +from macropodus.network.layers.non_mask_layer import NonMaskingLayer +from macropodus.network.layers.crf import CRF +custom_objects['AdaptiveEmbedding'] = AdaptiveEmbedding +custom_objects['AdaptiveSoftmax'] = AdaptiveSoftmax +custom_objects['NonMaskingLayer'] = NonMaskingLayer +custom_objects['CRF'] = CRF + +# layers +preprocessing = keras.preprocessing +applications = keras.applications +regularizers = keras.regularizers +initializers = keras.initializers +activations = keras.activations +constraints = keras.constraints +optimizers = keras.optimizers +callbacks = keras.callbacks +datasets = keras.datasets +wrappers = keras.wrappers +metrics = keras.metrics +backend = keras.backend +engine = keras.engine +layers = keras.layers +models = keras.models +losses = keras.losses +utils = keras.utils diff --git a/macropodus/base/__init__.py b/macropodus/base/__init__.py new file mode 100644 index 0000000..96bfef4 --- /dev/null +++ b/macropodus/base/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/12 23:04 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/base/seg_basic.py b/macropodus/base/seg_basic.py new file mode 100644 index 0000000..bec2d3b --- /dev/null +++ b/macropodus/base/seg_basic.py @@ -0,0 +1,148 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/28 20:17 +# @author : Mo +# @function: basic of segment, dictionary + + +from macropodus.preprocess.tools_common import load_json, save_json, txt_read +from macropodus.conf.path_config import path_dict_macropodus, path_dict_user +from macropodus.conf.path_config import path_macropodus_dict_freq_cache +from macropodus.conf.path_log import get_logger_root +from collections import defaultdict +import pickle +import time +import os + + +logger = get_logger_root() + + +class SegBasic: + def __init__(self, use_cache=True): + # time_start = time.time() + # 存在缓存则直接读取, 序列化加速缓存读取速度 + if use_cache and os.path.exists(path_macropodus_dict_freq_cache): + with open(path_macropodus_dict_freq_cache, "rb") as fpmc: + [self.dict_words_freq, self.num_words, self.dict_user] = pickle.load(fpmc) + fpmc.close() + # logger.info("seg: " + str(time.time()-time_start)) # 5.29, 5.26 + else: + self.dict_words_freq = defaultdict() + self.dict_user = {} + self.load_macropodus_dict() # 默认字典 + self.load_user_dict() # 用户字典 + # logger.info("seg: " + str(time.time() - time_start)) # 10.13, 10.33 + # 第一次跑macropodus, 序列化需要的缓存 + if use_cache and not os.path.exists(path_macropodus_dict_freq_cache): + with open(path_macropodus_dict_freq_cache, "wb") as fpmc: + pickle.dump([self.dict_words_freq, self.num_words, self.dict_user], fpmc) + + def load_macropodus_dict(self): + """ + 加载默认的基础字典 + :return: None + """ + dict_macropodus = load_json(path_dict_macropodus)[0] # (path_dict_jiagu)[0] # (path_dict_macropodus)[0] # 加载json字典文件 + dict_macropodus_def = defaultdict() # 转为defaultdict + for k,v in dict_macropodus.items(): + dict_macropodus_def[k] = v + self.dict_words_freq = dict_macropodus_def # {}词-词频字典 + + def load_user_dict(self, path_user=path_dict_user, type_user="json"): + """ + 加载用户词典 + :param path_user:str, like '/home/user.dict' + :return: None + """ + if not os.path.exists(path_user): + raise RuntimeError("your path_user is not exist!") + if type_user == "json": + self.dict_user = load_json(path_user)[0] # 加载json字典文件 + for k, v in self.dict_user.items(): + if k not in self.dict_words_freq: + self.dict_words_freq[k] = v # 更新到总字典, words_freq + else: + self.dict_words_freq[k] = self.dict_words_freq[k] + v # 更新到总字典, words_freq + self.num_words = sum(self.dict_words_freq.values()) + elif type_user == "txt": + words_all = txt_read(path_user) + for word_freq in words_all: + wf = word_freq.split(" ") # 空格' '区分带不带词频的情况 + if len(wf) == 2: + word = wf[0] + freq = wf[1] + else: + word = wf[0] + freq = 132 + if word not in self.dict_words_freq: + self.dict_words_freq[word] = freq # 更新到总字典, words_freq + else: + self.dict_words_freq[word] = self.dict_words_freq[word] + freq # 更新到总字典, words_freq + self.num_words = sum(self.dict_words_freq.values()) + elif type_user == "csv": + words_all = txt_read(path_user) + for word_freq in words_all: + wf = word_freq.split(",") # 逗号','区分带不带词频的情况 + if len(wf)==2: + word = wf[0] + freq = wf[1] + else: + word = wf[0] + freq = 132 + if word not in self.dict_words_freq: + self.dict_words_freq[word] = freq # 更新到总字典, words_freq + else: + self.dict_words_freq[word] = self.dict_words_freq[word] + freq # 更新到总字典, words_freq + self.num_words = sum(self.dict_words_freq.values()) + else: + raise EOFError + + def add_word(self, word, freq=132): + """ + 新增词典到词语, 不可持久化, 重载消失 + :param word: str, like '大漠帝国' + :param freq: int, like 132 + :return: None + """ + assert type(word) == str + if word in self.dict_words_freq: + self.dict_words_freq[word] = self.dict_words_freq[word] if freq !=132 else freq + else: + self.dict_words_freq[word] = freq + self.num_words += freq + + def delete_word(self, word): + """ + 删除词语, 不可持久化, 重载消失 + :param word_freqs: str, like '大漠帝国' + :return: None + """ + assert type(word) == str + if word in self.dict_words_freq: + self.num_words -= self.dict_words_freq[word] + self.dict_words_freq.pop(word) + + def save_add_words(self, word_freqs): + """ + 新增词语到用户词典, 可持久化, 重载有效 + :param word_freqs: dict, like {'大漠帝国':132} + :return: None + """ + assert type(word_freqs) == dict + for k, v in word_freqs.items(): + self.add_word(k, v) # 新增到总字典, 不持久化 + self.dict_user[k] = v # 新增到用户字典, 持久化 + save_json([self.dict_user], path_dict_user) + + def save_delete_words(self, words): + """ + 删除词语到用户词典, 可持久化, 重载有效 + :param word_freqs: list, like ['大漠帝国'] + :return: None + """ + assert type(words) == list + for w in words: + self.delete_word(w) # 删除到总字典, 不持久化 + if w in self.dict_user: self.dict_user.pop(w) # 删除到用户字典, 持久化 + save_json([self.dict_user], path_dict_user) diff --git a/macropodus/base/word2vec.py b/macropodus/base/word2vec.py new file mode 100644 index 0000000..2ae4256 --- /dev/null +++ b/macropodus/base/word2vec.py @@ -0,0 +1,64 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:52 +# @author : Mo +# @function: word2vec of gensim + + +from macropodus.conf.path_config import path_embedding_word2vec_char, path_macropodus_w2v_char_cache +from macropodus.conf.path_log import get_logger_root +import numpy as np +import gensim +import pickle +import time +import os + + +logger = get_logger_root() +gensim.logger.level=40 + + +class W2v: + def __init__(self, use_cache=True): + # time_start = time.time() + # 存在缓存则直接读取, 序列化加速缓存读取速度 + if use_cache and os.path.exists(path_macropodus_w2v_char_cache): + with open(path_macropodus_w2v_char_cache, "rb") as fpmc: + self.w2v_char= pickle.load(fpmc) + fpmc.close() + # logger.info("word2vec: " + str(time.time() - time_start)) # 0.12 + else: + # gensim加载词向量 + self.w2v_char = gensim.models.KeyedVectors.load_word2vec_format(path_embedding_word2vec_char) + # logger.info("word2vec: " + str(time.time() - time_start)) # 0.99, 0.78 + # 第一次跑macropodus, 序列化需要的缓存 + if use_cache and not os.path.exists(path_macropodus_w2v_char_cache): + with open(path_macropodus_w2v_char_cache, "wb") as fpmc: + pickle.dump(self.w2v_char, fpmc) + + def cosine(self, sen_1, sen_2): + """ + 余弦距离 + :param sen_1: numpy.array + :param sen_2: numpy.array + :return: float, like 0.0 + """ + if sen_1.all() and sen_2.all(): + return np.dot(sen_1, sen_2) / (np.linalg.norm(sen_1) * np.linalg.norm(sen_2)) + else: + return 0.0 + + def jaccard(self, sen_1, sen_2): + """ + jaccard距离 + :param sen1: str, like "大漠帝国" + :param sen2: str, like "Macropodus" + :return: float, like 0.998 + """ + try: + sent_intersection = list(set(list(sen_1)).intersection(set(list(sen_2)))) + sent_union = list(set(list(sen_1)).union(set(list(sen_2)))) + score_jaccard = float(len(sent_intersection) / len(sent_union)) + except: + score_jaccard = 0.0 + return score_jaccard diff --git a/macropodus/conf/__init__.py b/macropodus/conf/__init__.py new file mode 100644 index 0000000..82e2e84 --- /dev/null +++ b/macropodus/conf/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 23:59 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/conf/path_config.py b/macropodus/conf/path_config.py new file mode 100644 index 0000000..3081078 --- /dev/null +++ b/macropodus/conf/path_config.py @@ -0,0 +1,38 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 23:59 +# @author : Mo +# @function: path of macropodus + + +import sys +import os +path_root = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) +sys.path.append(path_root) + + +# path of basic +path_dict_macropodus = os.path.join(path_root, "data/dict/macropodus.dict") +path_dict_user = os.path.join(path_root, "data/dict/user.dict") +path_log_basic = os.path.join(path_root, "logs") + +# path of cache +path_macropodus_w2v_char_cache = os.path.join(path_root, 'data/cache/word2vec_char.cache') +path_macropodus_dict_freq_cache = os.path.join(path_root, 'data/cache/macropodus.cache') + +# path of embedding +path_embedding_word2vec_char = os.path.join(path_root, 'data/embedding/word2vec/w2v_model_wiki_char.vec') +path_embedding_bert = os.path.join(path_root, 'data/embedding/chinese_L-12_H-768_A-12/') +path_embedding_random_char = os.path.join(path_root, 'data/embedding/term_char.txt') +path_embedding_random_word = os.path.join(path_root, 'data/embedding/term_word.txt') +path_embedding_albert = os.path.join(path_root, 'data/embedding/albert_base_zh') + +# path of train data of ner people 1998 +path_ner_people_1998_train = os.path.join(path_root, "data/corpus/ner_people_1998/train.json") +path_ner_people_1998_valid = os.path.join(path_root, "data/corpus/ner_people_1998/dev.json") + +# path of train data of seg pku 1998 +path_seg_pku_1998_train = os.path.join(path_root, "data/corpus/seg_pku_1998/train.json") + +# path of training model save dir +path_model_dir = os.path.join(path_root, "data/model") diff --git a/macropodus/conf/path_log.py b/macropodus/conf/path_log.py new file mode 100644 index 0000000..deb0f29 --- /dev/null +++ b/macropodus/conf/path_log.py @@ -0,0 +1,50 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 23:59 +# @author : Mo +# @function: logger of macropodus + + +from macropodus.conf.path_config import path_log_basic +from logging.handlers import RotatingFileHandler +import logging +import time +import os + + +logger_level = logging.INFO +# log目录地址 +path_logs = path_log_basic # + '/logs' +if not os.path.exists(path_logs): + os.mkdir(path_logs) +# 全局日志格式 +logging.basicConfig(level=logger_level, + format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') +# 定义一个日志记录器 +logger = logging.getLogger("macropodus") +logger.setLevel(level = logger_level) +# 日志文件名,为启动时的日期 +log_file_name = time.strftime('macropodus-%Y-%m-%d', time.localtime(time.time())) + ".log" +log_name_day = os.path.join(path_logs, log_file_name) +# 文件输出, 定义一个RotatingFileHandler，最多备份32个日志文件，每个日志文件最大32K +fHandler = RotatingFileHandler(log_name_day, maxBytes = 32*1024, backupCount = 32) +fHandler.setLevel(logger_level) +# 日志输出格式 +formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +fHandler.setFormatter(formatter) +# 控制台输出 +console = logging.StreamHandler() +console.setLevel(logger_level) +console.setFormatter(formatter) +# logger加到handel里边 +logger.addHandler(fHandler) +logger.addHandler(console) + + +def get_logger_root(name="macropodus"): + """ + 全局日志引用 + :param name: str, name of logger + :return: object, logging + """ + return logging.getLogger(name) diff --git a/macropodus/data/__init__.py b/macropodus/data/__init__.py new file mode 100644 index 0000000..e099f67 --- /dev/null +++ b/macropodus/data/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/21 23:06 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/data/cache/__init__.py b/macropodus/data/cache/__init__.py new file mode 100644 index 0000000..80abf3c --- /dev/null +++ b/macropodus/data/cache/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/3 0:25 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/data/dict/macropodus.dict b/macropodus/data/dict/macropodus.dict new file mode 100644 index 0000000..a36157e --- /dev/null +++ b/macropodus/data/dict/macropodus.dict @@ -0,0 +1 @@ +[{"数学": 223238, "欧几里得": 1759, "西元": 10902, "前": 1455703, "三世纪": 748, "的": 116460885, "古希腊": 16517, "数学家": 13891, "现在": 2171588, "被": 5196571, "认为": 1984665, "是": 21925309, "几何": 33515, "之": 3541709, "父": 82884, "此画": 682, "为": 6578876, "拉斐尔": 3293, "作品": 438031, "雅典": 14289, "学院": 324669, "利用": 554482, "符号": 62276, "语言": 361311, "研究": 879825, "数量": 217411, "结构": 386126, "变化": 352268, "以及": 1011292, "空间": 367818, "等": 5050748, "概念": 187704, "一门": 45007, "学科": 96664, "从": 3335561, "某种": 66978, "角度": 201096, "看": 11542189, "属于": 614793, "形式": 269519, "科学": 265225, "一种": 1129066, "透过": 58328, "抽象化": 871, "和": 13035267, "逻辑推理": 2343, "使用": 1900016, "由": 2401490, "计数": 10433, "计算": 296164, "量度": 2990, "对": 5591048, "物体": 73227, "形状": 65620, "及": 3555222, "运动": 436824, "观察": 186758, "而": 3436436, "产生": 536607, "们": 343419, "拓展": 33753, "这些": 1133763, "为了": 843453, "公式化": 705, "新": 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"西塞罗": 961, "译自": 631, "希腊文": 2487, "此": 581764, "希腊语": 6037, "亚里士多德": 4711, "拿来": 33392, "万物": 23696, "皆": 125620, "数": 370132, "汉字": 53287, "表示": 707913, "大约": 158281, "于": 2535775, "中国": 2278556, "宋元": 2719, "多": 2564566, "象数": 249, "学": 731987, "含有": 183276, "今天": 730456, "上": 4529461, "意义": 240441, "秦九韶": 254, "九章": 2014, "永乐大典": 1052, "记": 304311, "即": 640498, "书": 968894, "宋代": 17345, "周密": 3127, "所著": 8793, "癸辛杂识": 49, "记为": 3008, "大略": 2296, "通": 259096, "轨": 25244, "明代": 21833, "柯": 55209, "尚": 145786, "迁": 37389, "著": 151770, "钥": 21719, "清代": 26080, "杜": 181759, "知": 187288, "耕": 17701, "拾遗": 2670, "丁": 278059, "取": 230897, "忠": 60061, "撰": 9657, "直到": 187315, "年": 13943273, "经过": 1222269, "中国数学": 357, "名词": 40412, "审查": 81460, "委员会": 195767, "算学": 561, "两": 724754, "状况": 153683, "后": 7157666, "确认": 230795, "含义": 45698, "历史": 528214, "奇": 217343, "普": 189749, "印加": 1985, "帝国": 96530, "时": 4854603, "所": 1316353, "工具": 450417, "玛雅": 7683, "数字": 204770, "有着": 119989, "久远": 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6839201, "简单": 574909, "地被": 7943, "概括": 28193, "方面": 694932, "到了": 462875, "初等": 3753, "代数": 20380, "三角学": 258, "大体": 18275, "完备": 9809, "变量": 54868, "使": 864491, "人们": 336120, "量": 270267, "互相": 119282, "图形": 51906, "变换": 32577, "微积分": 7574, "此时": 142266, "随着": 207962, "转向": 42930, "化为": 14139, "集合": 45155, "论": 135897, "数理逻辑": 967, "重心": 18750, "求解": 31324, "转变": 40218, "到": 3401482, "一般": 1357970, "形式上": 6432, "从古至今": 2415, "一直": 833795, "不断": 268477, "延展": 2603, "丰富": 198792, "相互作用": 13967, "都": 5243437, "受惠于": 691, "彼此": 74882, "有著": 23945, "并且": 472101, "据": 221994, "美国": 1085881, "数学会": 328, "通报": 15852, "月": 10475391, "期刊": 24183, "所说": 86735, "存放": 59982, "评论": 232805, "资料库": 9659, "论文": 107667, "书籍": 100134, "自": 595715, "创刊": 5310, "年份": 19188, "现已": 42051, "超过": 438802, "一百九十万": 32, "份": 158371, "而且": 1018337, "每年": 180768, "还": 2940764, "增加": 457557, "七万五千": 256, "学海": 985, "绝大部份": 864, "及其": 237184, "证明": 1370671, "美学": 17936, "牛顿": 15986, "发明者": 3602, "每当": 25625, "涉及": 100852, "困难": 163047, "用到": 45697, "去": 3193277, "解决": 570173, "这": 5018187, "往往": 184861, "一开始": 96074, "可见": 68715, "之后": 1087612, "天文学": 11059, "所有": 827777, "存在": 545452, "值得": 241187, "给出": 80882, "莱布尼兹": 615, "费曼": 2166, "发明": 70392, "路径": 63597, "积分": 90844, "这是": 1073055, "推理": 27124, "物理": 153939, "洞察": 4613, "二者": 31619, "产物": 35945, "弦": 44117, "引申出": 740, "一些": 1336809, "只": 1055119, "生成": 101468, "解答": 106573, "更多": 366884, "十分": 234396, "有用": 118368, "即使是": 37498, "最纯": 692, "亦有": 30783, "用途": 71770, "非比寻常": 661, "事实": 2284734, "诺贝尔": 20253, "奖得主": 4523, "维格纳": 286, "自然科学": 11254, "不可想像": 115, "有效性": 11461, "如同": 46330, "大多数": 142957, "爆发": 86070, "专业化": 7122, "分歧": 17724, "又": 2053358, "分成": 48647, "两大": 25118, "变成": 261257, "它们": 262710, "自身": 184489, "统计学": 13655, "电脑": 776597, "谈论": 21533, "优美": 28195, "内在": 31862, "一般化": 1061, "即为": 22640, "巧妙": 17174, "无限": 82488, "质数": 5150, "或者是": 107942, "加快": 54514, "数值": 52585, "方法": 1191116, "快速": 224823, "傅立叶": 2598, "高德": 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2007989, "特别": 566429, "术语": 24563, "胚": 8877, "积": 95605, "性": 591442, "专有名词": 2052, "专有": 4586, "原因": 769205, "将": 4681810, "逻辑": 113430, "要求": 966428, "现实": 208627, "舍弃": 7567, "得到": 551707, "更好": 221335, "结果": 835868, "很": 3669248, "重要": 813627, "一部份": 8827, "希望": 766951, "他们": 1869967, "系统化": 4381, "依": 124401, "推论": 6131, "下去": 147529, "避免": 238419, "不可": 227019, "靠": 293218, "直观": 24379, "推出": 161272, "错误": 235605, "情形": 68309, "出现": 1013615, "过": 1706195, "例子": 129861, "期许": 2601, "程度": 657935, "因": 1389815, "希腊人": 4732, "仔细": 91588, "论证": 22501, "时代": 326840, "所做": 21204, "十九世纪": 5427, "重新": 376548, "小心": 59462, "分析": 536900, "正式": 263109, "持续": 171571, "争论": 25971, "辅助": 103089, "度": 392678, "当": 1223871, "验证": 81361, "很难说": 8952, "传统": 313037, "思想": 203659, "证": 277961, "自明": 1118, "真理": 30140, "想法": 182225, "只是": 827241, "串": 61021, "导出": 22329, "公式": 85704, "有意义": 24934, "希尔伯特": 1669, "计划": 400114, "即是": 29385, "想": 11104044, "放在": 384705, "坚固": 11197, "基础上": 92209, "依据": 164203, "哥德尔": 810, "每": 777940, "相容": 3302, "能": 2996138, "蕴涵": 2778, "皮亚诺": 499, "必": 129340, "决定": 456940, "因而": 94238, "最终": 305337, "公理化": 573, "不可能": 182316, "尽管如此": 8266, "常常": 96922, "想像成": 349, "在此": 156420, "下": 2487414, "叙述": 30988, "写成": 23885, "卡尔": 38962, "弗里德里希": 4139, "高斯": 11949, "称": 437892, "皇后": 52568, "拉丁": 15747, "原文": 44140, "德语": 36321, "对应": 111791, "单字": 3539, "实际上": 154645, "本来": 210824, "这个": 3815215, "无疑": 39641, "问": 833905, "确实": 532681, "限定": 38664, "事": 818179, "若": 383185, "世界时": 2571, "至少": 239492, "不会": 1153956, "爱因斯坦": 15140, "如此": 242214, "描述": 193923, "定律": 27048, "越": 423017, "不确定": 26620, "越是": 22868, "确定": 340952, "的话": 1212509, "哲学家": 15562, "经验": 316662, "具": 130734, "可否": 13291, "因此": 632842, "不是": 2006848, "波普尔": 735, "年代": 247527, "重大": 173467, "显示": 423899, "不能": 1379262, "归并": 1049, "推断": 12606, "大部份": 11606, "生物学": 19497, "一样": 1132348, "假设": 72869, "演绎": 28633, "变得": 165411, "接近": 119281, "猜测": 29827, "看起来": 128448, "然而": 265678, "思想家": 8018, "著名": 195356, "拉卡托斯": 47, "提供": 1237655, "关于": 632061, "版本": 320534, "另": 497235, "观点": 171759, "符合": 240823, "事实上": 79613, "物理学家": 14172, "齐曼": 65, "公众": 78178, "情况": 1789064, "很多": 1622803, "相同": 219763, "地方": 792553, "尤其是": 183320, "所得": 124476, "探索": 68511, "直觉": 12916, "实验": 186500, "建构": 8427, "扮演": 57333, "角色": 263393, "重要性": 36894, "正": 490424, "模拟": 88372, "越来越": 235920, "加重": 48704, "减轻": 128887, "缺点": 86902, "史蒂芬": 7143, "沃尔夫": 8317, "勒": 142666, "姆": 140732, "著作": 54205, "提出": 447802, "应": 952334, "视为": 64895, "态度": 201120, "并不": 350254, "一致": 132390, "觉得": 1366880, "科学家": 65336, "那些": 466339, "时常": 32694, "工作": 1720195, "基本上": 160655, "低估": 9904, "做为": 33732, "七大": 7035, "博雅": 3426, "有人": 487207, "忽略": 53121, "之间": 566388, "关联": 41052, "假装": 31272, "没": 1987005, "看到": 985683, "影响": 891794, "进而": 46507, "促进": 153054, "两种": 214043, "差异": 112905, "哲学": 79610, "创造": 128856, "艺术": 242981, "或是": 157734, "争议": 72185, "大学": 863690, "院系": 6547, "划分": 43957, "常见": 166609, "数学系": 5627, "指出": 129697, "两个": 769190, "看作": 18818, "紧密": 28258, "联系": 447725, "而非": 41105, "大体上": 5713, "合作": 302947, "细节": 114839, "分开": 69938, "亦是": 25025, "众多": 83697, "议题": 15020, "其中": 824438, "各": 594774, "完全": 560618, "著重": 2661, "演算": 5454, "运算": 37735, "有如": 9104, "反映": 102822, "算盘": 2864, "如上所述": 937, "最先": 17004, "商业": 182630, "四种": 24356, "大致": 69380, "广泛": 123206, "子领域": 180, "连": 472637, "除了": 439813, "上述": 1823958, "关注": 293503, "之外": 122640, "核心": 178313, "连结": 196291, "近代": 32039, "不确定性": 9383, "严格": 115075, "阐明": 6651, "专注于": 14161, "置": 61021, "架构": 42448, "第二": 677354, "所属": 49736, "或许是": 16096, "最": 1783989, "广为流传": 3424, "成果": 63080, "总": 412198, "而又": 27149, "真的": 902280, "递归": 4890, "模型": 153773, "密切": 38151, "关连性": 233, "千禧年": 1554, "大奖": 38129, "难题": 26446, "纯粹": 44354, "起于": 5071, "熟悉": 100353, "自然数": 5369, "整数": 24680, "更深": 13179, "性质": 116849, "数论": 2430, "详细": 227471, "费马": 1379, "最后": 897161, "广为": 5231, "探讨": 83251, "未": 921260, "解": 192156, 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7670, "方程": 49422, "集": 2408688, "李群": 1056, "空间结构": 1372, "分支": 36756, "最大": 434510, "已久": 10191, "庞加莱": 1003, "色": 200874, "俄罗斯": 168418, "格": 283475, "戈里": 2443, "佩": 92403, "雷": 286045, "尔": 425886, "曼": 148743, "凯": 138013, "尼斯": 16228, "阿佩尔": 202, "沃夫": 2010, "冈": 44760, "哈肯": 275, "从来": 119230, "没有": 3981251, "人力": 28524, "普遍": 116541, "有利": 36646, "诞生": 55064, "于此": 26410, "实": 140093, "变": 296336, "复分析": 449, "等价": 9745, "黎曼": 2786, "未决": 640, "便是": 49309, "泛函": 2070, "注重": 63615, "量子力学": 7754, "与其": 113975, "变化率": 1102, "微分方程": 5734, "自然界": 12962, "现象": 263005, "动力": 159692, "混沌": 13260, "既": 172288, "又是": 115963, "行为": 1617367, "离散": 9967, "处": 947238, "总称": 6552, "复杂性": 7302, "资讯": 56329, "检验": 661358, "现": 236083, "有力": 27497, "图": 858142, "灵机": 1272, "理论研究": 4641, "易": 252965, "有些": 518700, "花费": 54642, "太多": 286347, "仍然": 174483, "不为": 22614, "可行": 27702, "尽管": 130427, "硬体": 3168, "进步": 91975, "专注": 23021, "特定": 91110, "媒介": 14009, "资料": 588991, "总量": 24466, "熵": 6255, "相对": 225721, "有名": 45056, "否定": 33373, "工商业": 3669, "及其他": 40105, "机率": 15164, "机会": 322909, "成分": 322563, "进行": 2040480, "调查": 297326, "统计": 123790, "家": 1182478, "份子": 6813, "什么": 4259737, "限": 43575, "算不出": 346, "舍": 74913, "入": 294739, "误差": 27856, "来源": 227321, "奖项": 44465, "菲尔": 14327, "兹": 86222, "奖牌": 11018, "正面": 64403, "数学奖": 471, "奖": 141951, "四年": 83342, "颁奖": 28747, "一次": 682422, "诺贝尔奖": 9713, "国际上": 14426, "阿贝尔": 1991, "主题": 140683, "创新": 139178, "或已": 2860, "成熟": 148091, "德国": 246578, "大卫": 35679, "一连串": 8558, "极高": 22877, "名望": 1962, "九个": 21458, "一新": 4565, "七个": 21825, "发表": 155636, "一百万": 8886, "美元": 222842, "奖金": 40108, "当中": 108405, "只有": 794044, "重复": 101500, "国际": 483134, "联盟": 170882, "大会": 79111, "颁发": 32112, "颁给": 1727, "卓越": 60085, "贡献": 75315, "年轻": 169738, "每次": 289167, "最多": 176696, "人": 6571296, "得奖": 11213, "得奖者": 1817, "须": 118507, "元旦": 16846, "未满": 20197, "四十岁": 3985, "获得": 632131, "加拿大": 101884, "约翰": 68388, "查尔斯": 14702, "设立": 103222, "数学界": 689, "基金会": 33619, "以色列": 46957, "创始人": 38174, "里卡多": 1435, "外交家": 1599, "实业家": 1652, "慈善家": 2105, "共同": 305282, "誉为": 26083, "最高": 290398, "荣誉": 60151, "挪威": 26919, "王室": 12996, "向": 1955573, "杰出": 30149, "纪念": 80282, "尼尔斯": 1424, "亨利": 26065, "克": 1299457, "二百": 7552, "周年": 43407, "诞辰": 3904, "政府": 493481, "宣布": 150583, "此种": 15065, "数额": 232050, "同": 622818, "相近": 21941, "此奖": 469, "也是": 1267641, "拨款": 11638, "亿": 247413, "克朗": 3156, "启动": 272950, "资金": 315996, "扩大": 73417, "吸引": 115811, "年轻人": 81580, "从事": 183954, "目的": 466067, "参见": 161725, "游戏": 1137191, "列表": 197009, "算": 271590, "经": 4020320, "十": 271158, "竞赛": 39378, "数学题": 8831, "注记": 1362, "参考": 671875, "书目": 16870, "百科全书": 7681, "牛津": 9896, "词典": 26793, "网址": 43155, "英文版": 21110, "漫游": 14679, "在线": 157338, "维基": 22034, "百科": 73495, "交换": 63699, "条目": 44372, "计算机科学": 9098, "新闻网": 7709, "志": 144553, "香港": 568528, "科技": 257830, "网": 486755, "数学史": 636, "为主": 175163, "网站": 457838, "怎样": 498803, "研习": 3147, "本科": 148302, "课程": 211522, "文化": 495764, "高质量": 18558, "带有": 84623, "普及性": 622, "文章": 310172, "面向": 50180, "大学生": 97252, "老师": 628569, "研究生": 121198, "中学": 170114, "学生": 778487, "资源": 299391, "互联网": 175738, "教学": 251774, "视频": 558685, "英汉": 4099, "对照": 24820, "用语": 13635, "苏格拉底": 4562, "死": 498586, "雅克": 5991, "路易": 12551, "所绘": 1474, "根本": 270311, "价值": 1504099, "理智": 26196, "心灵": 60450, "批判": 30806, "理性": 53112, "引申为": 1920, "信仰": 66814, "简介": 81628, "词语": 34010, "爱": 1356051, "智慧": 98812, "译为": 9949, "朋友": 875657, "派生": 3186, "组合": 147992, "成": 636376, "毕达哥拉斯": 1183, "引入": 45073, "哲": 45510, "起源": 32640, "很早": 8342, "孔门": 496, "古圣": 386, "先哲": 586, "哲人": 1583, "专指": 2805, "善于": 34172, "思辨": 2911, "精深": 1445, "者": 722644, "近世": 1286, "谓": 31102, "易经": 7411, "讨论": 196533, "形而上学": 3125, "中文": 190040, "名称": 182406, "取自": 10431, "系辞": 461, "上传": 56991, "形而上": 1382, "之道": 21417, "形而下": 405, "器": 344766, "一语": 2286, "日本": 777262, "启蒙": 16257, "西周": 7989, "百": 172755, "新论": 569, "首先": 578060, "汉文": 4423, "翻译": 184565, "英国": 353010, "罗素": 6153, "我": 31274639, "理解": 336273, "来说": 508231, "乃是": 9948, "介乎": 2570, "神学": 10257, "东西": 944067, "着": 1529930, "迄今": 10222, "仍": 363306, "肯定": 417645, "事物": 84036, "像": 904522, "诉": 25410, "之于": 9886, "权威": 43629, "不论是": 19769, "还是": 2241508, "启示": 13878, "一切": 304596, "确切": 20381, "超乎": 4488, "教条": 3172, "一片": 101972, "受到": 292874, "双方": 381422, "攻击": 238277, "无人": 154298, "片": 439497, "胡适": 5471, "哲学史": 2786, "大纲": 19218, "凡": 93792, "人生": 299625, "切要": 319, "根本上": 11890, "想要": 422152, "寻": 81560, "叫做": 94903, "源自": 20459, "文明": 115264, "相似": 81408, "论题": 1838, "东亚": 23799, "南亚": 7947, "称之为": 40726, "东方": 127017, "北非": 6871, "中东": 29041, "欧洲": 216637, "互动": 59660, "看法": 109650, "实质上": 8098, "必须": 652456, "回答": 650779, "命题": 21841, "后现代主义": 1721, "把": 2592315, "定义为": 14632, "学术": 82862, "其它": 214578, "总和": 14144, "世界": 988861, "本质": 61980, "解释": 258705, "很大": 323044, "接受": 498567, "世界观": 19455, "相互": 159073, "涉及到": 30632, "对象": 199012, "具有": 664951, "方法论": 7672, "功能": 677396, "承": 53333, "述": 16444, "批判性": 2729, "条理": 7071, "辩论": 29158, "实践": 116904, "所提": 7787, "大概": 331512, "归类为": 4402, "三类": 12054, "分别": 456763, "伦理学": 3244, "认识论": 2033, "知识论": 443, "有意思": 55308, "蒙": 153677, "现出": 1757, "论调": 4151, "技巧": 121778, "认定": 1252137, "不可知论": 928, "世界上": 152076, "终究": 15721, "提高": 444611, "主": 718831, "分为": 198809, "逻辑学": 3156, "宇宙论": 601, "价值论": 384, "特殊": 197476, "后设": 568, "宗教": 101254, "科学哲学": 769, "政治": 311854, "法律": 384556, "社群": 11534, "各种": 584771, "流派": 13422, "通过": 1379481, "每个": 307551, "组织": 518819, "古典": 37007, "中世纪": 18014, "印度": 172424, "源远流长": 2572, "吠陀": 1449, "公元前": 35806, "全盛": 2943, "思想界": 441, "百花齐放": 2194, "佛教": 71940, "释迦牟尼佛": 3403, "耆那": 757, "教": 254021, "摩那": 194, "阿耆多": 21, "翅": 16406, "钦": 29919, "婆罗": 1160, "波": 278053, "拘": 4958, "陀": 16311, "迦": 35137, "旃": 1658, "延": 70194, "富兰那": 42, "迦叶": 1347, "派": 172255, "部分": 714561, "东周": 4046, "诸子百家": 1471, "广为人知": 3995, "孔子": 38137, "儒家": 17268, "老子": 44288, "道家": 9232, "墨子": 4819, "墨家": 2324, "晚期": 29986, "法家": 2801, "代表": 403716, "农家": 10260, "阴阳家": 680, "名家": 10822, "名声": 15284, "显在": 229, "秦朝": 6941, "焚书坑儒": 863, "外": 577794, "不再": 179113, "活跃": 48677, "当代": 43765, "亚洲": 129130, "一定": 962123, "作用": 561221, "但是": 2073295, "学理": 1598, "争辩": 4789, "是否": 725837, "归为": 5106, "罗马": 73784, "柏拉图": 6092, "左": 387991, "右": 307526, "裁剪": 6659, "圣": 209163, "齐": 180014, "奥": 237717, "约": 857637, "公元": 42327, "希腊": 51265, "希腊化": 1145, "有时候": 151738, "主义": 67316, "基督教": 48523, "加入": 369665, "神话": 58500, "诗歌": 37715, "脱颖而出": 7365, "逐步": 57081, "宇宙": 102572, "组成": 209740, "本源": 2696, "独立": 253996, "最早": 103310, "不管": 175919, "认识": 325302, "正确": 264759, "之所以": 63290, "有别于": 5911, "迷信": 18753, "在于": 179450, "辅佐": 5276, "证据": 2143959, "归纳出": 1191, "诸如": 27510, "利": 306992, "泰勒斯": 1236, "那": 1988404, "西曼": 1017, "德和": 3714, "克西": 3523, "尼": 236552, "色诺芬": 315, "一元论": 465, "来自": 257898, "水": 928045, "德": 498279, "物": 315382, "气": 194815, "学说": 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145290, "偶": 51143, "男": 441214, "女": 558017, "静": 103816, "动": 159107, "直": 165870, "曲": 225042, "光明": 44013, "黑暗": 71360, "善": 80930, "恶": 119995, "方": 440540, "圆": 138155, "至此": 27837, "地位": 147925, "受": 598139, "造成": 906401, "加以": 49135, "差别": 80434, "意见": 633196, "矛盾": 134693, "关键": 233061, "特质": 18863, "依靠": 56580, "诉诸": 2530, "反诘法": 24, "反驳": 23408, "辩证法": 2849, "主要用": 2452, "善良": 47864, "公平": 46491, "正义": 45519, "道德": 87859, "分解成": 4131, "一系列": 86408, "疑问": 43112, "提取": 286128, "找到": 492739, "答案": 434383, "极大": 53356, "看出": 77717, "假说": 6900, "阶段": 263776, "直接": 893389, "过人": 10955, "深受": 20637, "整个": 305049, "来源于": 35683, "其一": 54024, "永远": 205871, "不要": 1241896, "坏事": 15394, "其二": 42539, "内心": 112826, "绝": 71527, "相反": 64657, "客观性": 3969, "试图": 72649, "推翻": 19961, "智者": 6243, "主观": 40671, "然后": 1470740, "行事": 11357, "方向": 654626, "回复": 134958, "何物": 1726, "说": 4043627, "得": 1167098, "开创": 16352, "奠定": 17909, "后世": 16460, "影响力": 47843, "特别是": 171320, "阿尔": 56142, "弗雷": 15743, 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"青壮年人": 91, "群体": 70148, "典雅": 7708, "纯文学": 736, "严肃": 33173, "俗文学": 106, "大众": 119624, "史传": 638, "纪实": 11716, "奇幻": 27306, "报导": 28390, "体裁": 4959, "古典文学": 2435, "文文": 5238, "文言文": 8874, "发达": 53234, "并称为": 3592, "纪": 105158, "尧": 27114, "阿波": 3282, "奈尔": 2603, "图象": 6948, "排列": 41113, "视觉": 70505, "形象": 139604, "想像": 18058, "抒情": 10897, "强烈": 122382, "悠久": 20315, "国度": 11041, "总集": 2163, "长篇": 10282, "乐府": 4626, "格律诗": 558, "唐末": 1464, "元曲": 1274, "五四": 7568, "新诗": 5212, "历代": 21828, "民间": 78383, "歌谣": 9191, "构成": 778324, "无以伦比": 417, "节奏性": 216, "本质上": 22213, "叙事诗": 1212, "但丁": 3426, "神曲": 7531, "歌德": 3940, "浮士德": 1394, "赋": 41661, "半": 282195, "文体": 10508, "篇幅": 10234, "而来": 45261, "代": 271692, "名作": 4289, "奏议": 444, "文告": 594, "后代": 27680, "应用文": 3119, "需求": 241594, "有的": 375165, "公告": 70110, "广告": 237744, "小品文": 628, "某": 8686927, "八股文": 911, "骈文": 869, "句式": 5264, "四六": 2132, "句": 282834, "对仗": 2026, "随笔": 5614, "兼有": 5217, "议论": 11387, "笔记": 52504, "游记": 17884, "古老": 38130, "感情": 240433, "用于": 446657, "舞台": 67088, "表演": 118556, "京剧": 25130, "昆剧": 487, "部份": 36830, "伴随": 20985, "舞蹈": 88516, "歌剧": 15443, "音乐剧": 12752, "目前": 753289, "最早期": 1642, "悲剧": 37955, "喜剧": 69986, "悲喜剧": 419, "叙事性": 454, "地表现": 1650, "社会生活": 6890, "庄子": 14208, "外物": 2105, "饰": 180193, "干": 388073, "县令": 5375, "矣": 35552, "寓言": 7880, "滥觞": 739, "汉末": 1833, "六朝": 3590, "唐": 306806, "传奇": 195288, "话本": 900, "拟": 55213, "传至": 4421, "书后": 5283, "金瓶梅": 3841, "红楼梦": 35122, "名著": 18363, "聊斋志异": 2250, "长度": 89160, "区分为": 1987, "极": 187195, "短篇": 10640, "书信": 7692, "字数": 9981, "较少": 34033, "话": 408069, "诗话": 2041, "词话": 571, "诗文": 7255, "回忆录": 7833, "科举": 10669, "考试": 407938, "排": 161613, "偶有": 4531, "文学奖": 8761, "优秀": 162751, "表扬": 15347, "广": 187696, "国籍": 22681, "新进": 3312, "遗嘱": 18125, "成立": 282318, "一项": 108804, "获奖": 30366, "为何": 116619, "不分": 26625, "纽": 18217, "塔特": 4365, "布克": 2937, "国际奖": 357, "卡夫卡": 2517, "型式": 5226, "形态": 65055, "影片": 119207, "肥皂剧": 1496, "多半": 23118, "衍生": 17844, "绘本": 12642, "漫画书": 2593, "图画": 11690, "创作者": 6140, "制造": 185052, "效果": 610998, "第一人称": 5937, "人称": 15282, "感受到": 44748, "犯罪": 1121718, "意外": 96383, "塞": 140173, "缪": 24325, "理查森": 631, "写": 721966, "信件": 10619, "书信体": 196, "大家": 1002698, "再次": 291053, "以往": 43353, "这么": 575825, "手段": 292803, "有点": 468382, "军事": 131467, "战略": 118243, "战术": 48102, "中用": 11080, "隐喻": 5747, "明喻": 388, "省略": 14661, "寓": 9097, "单纯": 89032, "谐音": 10178, "意识流": 1380, "批评者": 2643, "其他人": 64573, "评估": 136640, "改进": 66680, "种类": 81222, "背后": 94325, "层面": 48035, "注解": 9838, "隆": 116817, "溪": 125869, "兴衰": 2852, "一览表": 3398, "鉴赏": 7737, "左翼": 13056, "文艺工作者": 481, "叙事学": 251, "外国": 79962, "文学艺术": 2230, "新时期": 3574, "思想史": 1977, "比较文学": 1220, "红学": 1138, "剧作家": 3981, "散文家": 1384, "小说家": 9055, "批评家": 2049, "吉": 263242, "热": 286883, "历史学家": 11091, "欧文": 12862, "现代汉语": 5317, "词汇": 59699, "史": 224861, "今人": 3288, "未来": 318116, "同属": 6212, "精神文明": 3182, "第二个": 81142, "又称": 70611, "简称": 153992, "隶属于": 25980, "密切相关": 8910, 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"改以": 3620, "地主": 25424, "所得税": 23612, "自由放任": 483, "写下": 25015, "国富": 1967, "起始点": 987, "本书": 263061, "生产性": 1486, "好处": 114119, "乡村": 87027, "都市": 67261, "持有人": 8079, "途径": 92408, "报酬": 35083, "体会": 56695, "说道": 29479, "尽力": 18323, "竭力": 4824, "年收入": 10079, "尽量": 175468, "打算": 120364, "不知道": 780904, "宁愿": 16605, "盘算": 1898, "一双": 33588, "看不见": 29344, "手": 413608, "引导": 83921, "原先": 28731, "无意": 15234, "意料之外": 2438, "好事": 33572, "佯装": 2919, "心目中": 17268, "国防": 25877, "司法": 583385, "造福": 4359, "全社会": 5587, "水坝": 5290, "教科书": 21264, "递减": 6880, "生活水平": 10622, "预言": 18568, "倍增": 3874, "粮食": 45910, "渐进": 3725, "暴增": 1643, "大幅": 41601, "退步": 3842, "过剩": 11116, "存款": 105415, "租金": 29673, "上涨": 56780, "减低": 8399, "优势": 189365, "他国": 4486, "优越": 15434, "最好": 746689, "交换价值": 354, "难度": 96449, "资本论": 2040, "剩余价值": 2322, "剥削": 7978, "再生产": 1338, "挑战": 115786, "英格兰": 49249, "斯坦利": 1939, "杰文斯": 113, "格尔": 14899, "瑞士": 46040, "昂": 45648, "瓦尔": 17280, "钻石": 55147, "悖论": 5428, "促": 24075, "骨架": 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"好几个月": 3707, "听任": 4193, "用为": 1228, "密谋": 8595, "机密": 17559, "充当": 14736, "惠州": 13361, "重启": 89260, "人脉": 15661, "庚子": 3762, "拳": 69242, "乱": 144068, "引来": 11059, "八国联军": 3163, "入侵": 42309, "托付给": 1875, "日籍": 1259, "改革派": 1155, "永年": 2323, "三合会": 963, "大老": 2705, "扇": 47107, "两广": 3525, "失序": 386, "借此机会": 1995, "行省": 4322, "美利坚": 5536, "合众国": 3558, "答应": 62576, "发觉": 18394, "陷阱": 23009, "询": 7594, "召唤": 54650, "搭船": 485, "随行者": 68, "念头": 23860, "赶赴": 9542, "求见": 625, "西贡": 6645, "法属": 4098, "印度支那": 1894, "被禁": 3810, "三洲田": 71, "盐田区": 1394, "史称": 7475, "沙湾": 4193, "大败": 7982, "队伍": 81210, "直抵": 948, "镇": 1362999, "距": 62784, "接获": 2496, "挥兵": 121, "厦门": 54035, "陌生": 39095, "境": 34786, "行军": 5686, "清兵": 3132, "追击": 13322, "慑于": 291, "据点": 11153, "龙头": 21268, "只得": 11375, "逃逸": 72926, "丢下": 4367, "归巢": 835, "按计划": 2900, "新安": 10482, "虎门": 5314, "义军": 2538, "该地": 21222, "枪支": 145770, "弹药": 25507, "补给": 13578, "急电": 663, "孙孙": 550, "致电": 12767, "速": 114981, "枪弹": 12535, "采购": 71058, "中村": 12204, "从中渔利": 859, "所购": 7305, "告终": 6919, "左右手": 3308, "爆炸案": 2470, "脱逃": 4244, "教室": 50627, "击毙": 6413, "用餐": 10695, "卒": 16748, "滔天": 1241, "三十三年": 1742, "落花": 9008, "章炳麟": 222, "邀": 179396, "亡国": 5527, "二百四十": 461, "纪念会": 387, "河内": 8806, "停留": 43410, "大月": 1068, "薰": 9687, "婚约": 3811, "转到": 53147, "公立": 22987, "英": 173045, "女子学校": 737, "婚礼": 55500, "怀上": 6009, "后生": 3154, "宫川": 880, "富美": 1485, "青山": 28083, "学员": 40065, "为求": 2734, "扬弃": 731, "洪门": 1635, "致公堂": 107, "棍": 38673, "带著": 8231, "堂口": 568, "三藩市": 1457, "伪造": 103362, "州政府": 3280, "申": 72846, "住在": 62587, "库拉": 2773, "见证人": 4187, "夏威夷群岛": 1351, "证明书": 80819, "亲友们": 609, "证词": 6866, "举报": 74488, "扣留": 10234, "审问": 2687, "自述": 6134, "证言": 1130325, "三四岁": 1119, "带回": 32527, "原件": 44881, "藏于": 9699, "旧金山": 13493, "档案局": 1038, "大元帅府": 376, "纪念馆": 13327, "移民局": 4932, "美国旧金山": 1199, "华裔": 11484, "报人": 557, "伍": 73897, "保释": 2223, "方才": 3844, "委托": 112471, "席": 59641, "律师事务所": 10027, "上诉": 35286, "商务": 69031, "劳工部": 730, "复议": 7368, "属地": 4346, "拿": 456009, "疆": 6893, "动身": 1688, "巡回": 16872, "十多个": 2231, "首度": 13451, "博得": 4206, "好感": 32971, "檀": 12772, "新报": 1543, "日报": 24077, "携": 31805, "步": 141561, "发科": 878, "楚": 89452, "留学生": 38389, "豸": 757, "胡瑛": 146, "汤芗铭": 229, "国华": 2370, "十多": 9649, "应邀": 5418, "倭": 4695, "克拉": 23093, "结伴": 3543, "喝": 383881, "咖啡": 89647, "离席": 841, "盗走": 487694, "箧": 1629, "入盟": 380, "殖民": 17223, "信函": 6375, "得手": 31141, "清国": 1063, "告密": 1326, "宝": 379120, "琦": 18325, "你们": 652538, "叛": 5568, "陷害": 7034, "何在": 9681, "盗取": 26431, "后称": 1847, "今后": 43703, "好好": 220413, "念书": 7721, "安分守己": 746, "胡闹": 2120, "仲": 50437, "亲戚": 62540, "赶到": 70024, "疏通": 12252, "宣誓书": 98, "烧掉": 5371, "仲夏": 1565, "邮局": 11840, "寄还": 77, "法国外交部": 162, "交涉": 8491, "示意": 8156, "油画": 14073, "远东": 13054, "当即": 11833, "杨度": 425, "会中": 2577, "演说": 8547, "轰动": 7141, "会场": 8619, "富士见楼": 4, "饭": 153690, "河岸": 5102, "容纳": 16555, "宋教仁": 1077, "逾": 15997, "云集": 7022, "大厅": 57630, "簇拥": 1465, "门外": 27497, "届时": 12557, "爆满": 3443, "后来者": 2130, "络绎不绝": 1776, "拥挤": 14332, "吏": 15831, "封门": 1382, "诸人": 1237, "喧哗": 3387, "开门": 40547, "进场": 11616, "牵线": 1180, "华兴会": 213, "蔡元培": 1963, "吴敬恒": 106, "爱国": 31377, "学社": 1545, "张继": 2257, "青年会": 1792, "日本国会": 445, "阪": 6038, "本金": 87644, "宅邸": 1906, "宣读": 139497, "军政府": 3717, "复将": 101, "定立": 390, "党员": 31528, "本省": 5977, "推举为": 1181, "执行部": 526, "庶务": 711, "形同": 2169, "机关刊物": 288, "民报": 705, "发刊词": 206, "康有为": 2755, "改良派": 291, "第二十四": 2679, "期后": 2187, "开宗明义": 681, "著手": 2720, "满人": 1808, "关外": 1928, "东夷": 1732, "奴役": 4299, "汉人": 14355, "二百六十": 411, "任之": 484, "秋天": 42728, "编定": 515, "创立者": 2006, "誓": 9579, "军法": 1123, "约法": 1043, "三道": 6119, "执笔": 3433, "松散": 6981, "为敌": 2531, "门徒": 5036, "排满": 799, "种族主义": 2817, "全然": 4846, "诸如此类": 4672, "敌视": 2553, "第三次": 29943, "第九次": 1149, "马赛": 6189, "古风": 9325, "井": 128363, "所言": 9274, "素日": 181, "开诚布公": 910, "虚心": 5636, "待人": 7323, "作事": 513, "近于": 1676, "难堪": 4638, "一个月": 168984, "离境": 1911, "善意": 13195, "日方": 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220, "金刚": 64399, "陆机": 851, "陆云": 445, "文豪": 1969, "左联": 379, "鸳鸯": 12989, "郭沫若": 4324, "茅盾": 2418, "巴金": 3925, "叶圣陶": 1471, "郁达夫": 1072, "王造时": 86, "丁玲": 682, "张爱玲": 5756, "叶辛": 66, "王安忆": 715, "三重": 9005, "一举成名": 1452, "韩寒": 18133, "特立独行": 2958, "摇篮": 6961, "华文": 5432, "秦文君": 122, "赵丽": 741, "新宁": 1218, "许纪霖": 52, "骏": 39337, "落落": 3653, "画风": 18934, "昉": 1302, "书画": 12160, "吴昌硕": 437, "李叔同": 649, "花鸟": 5446, "鱼虫": 1020, "山湾": 964, "画馆": 136, "张大千": 1345, "刘海粟": 445, "丰子恺": 1026, "颜": 100254, "木刻": 1261, "连环画": 2724, "三毛": 10225, "展馆": 3681, "上海美术馆": 172, "莫干山": 1807, "田子": 1118, "创意": 61259, "画院": 970, "国画": 5356, "艺术界": 1472, "双年展": 1373, "独角戏": 1257, "说笑": 2242, "大世界": 5990, "游乐场": 8633, "徽班": 269, "皮黄": 321, "评弹艺术": 15, "周信芳": 355, "盖叫天": 143, "梅兰芳": 3669, "嵊县": 310, "嵊州": 1550, "唱腔": 4003, "沪剧": 965, "剧团": 9754, "上海京剧院": 156, "上海昆剧团": 41, "剧院": 16344, "昆剧团": 103, "借助于": 3093, "争锋": 10324, "选秀": 16271, "滑稽剧团": 94, "娘舅": 1809, "连演": 216, "长寿": 26783, "马戏": 1754, "说书": 2285, "锣鼓": 1752, "地区性": 2297, "第二批": 6739, "非物质": 5248, "大剧院": 3906, "名剧": 654, "官场": 7298, "黑奴": 1795, "吁": 3053, "天录": 308, "兰心": 659, "公演": 18312, "多用": 15326, "麻雀": 15358, "小孩": 197380, "歌舞剧": 856, "音乐院": 300, "上海戏剧": 1886, "上海音乐厅": 129, "百老汇": 5172, "悲惨": 12408, "普契尼": 474, "波西米亚人": 97, "图兰朵": 643, "赖声川": 444, "暗恋": 19316, "桃花源": 3924, "上海滩": 13855, "怪诞": 2128, "刽子手": 2094, "艺海": 563, "精彩": 78241, "国门": 4066, "巡回演出": 2254, "管乐": 1226, "管乐队": 212, "即今": 3745, "河畔": 19125, "夜来香": 1596, "何日": 4595, "玫瑰": 62353, "爱你": 201191, "恭喜": 28714, "明月": 45427, "千里": 32636, "相思": 25724, "流行于": 3921, "鉴于": 52503, "音乐人": 11972, "毕业歌": 862, "大刀": 7764, "聂耳": 1242, "少有": 7133, "音乐节": 9968, "廖昌永": 874, "高雅": 9048, "帕尔曼": 265, "小泽征尔": 301, "马友友": 419, "爱乐乐团": 1660, "交响音乐会": 215, "辰": 46392, "植物园": 7806, "赏花": 2123, "乐享": 1530, "参演": 14175, "不俗": 8121, "流行乐": 2408, "榜单": 14159, "风云榜": 2367, "毛阿敏": 2805, "胡彦斌": 2926, "明星": 117831, "流行乐坛": 829, "乐队": 53421, "休斯敦": 3574, "翁": 52849, "凯莉": 7067, "林肯": 157436, "比吉斯": 111, "海莉": 1616, "薇": 29546, "滨崎步": 1851, "滚石": 4644, "泰勒": 16108, "演唱会": 86112, "亚细亚": 1779, "摄制": 2957, "故事片": 10129, "电影业": 1551, "阮玲玉": 919, "周璇": 1133, "赵丹": 631, "珠": 84623, "晖": 15824, "家喻户晓": 4455, "被整": 798, "业界": 19198, "制片厂": 4831, "译制": 888, "南征北战": 2450, "红色娘子军": 2108, "鸡毛信": 593, "蝌蚪": 4438, "妈妈": 480439, "大闹": 12515, "哪吒": 5996, "闹": 56978, "海天": 5648, "奇谭": 9098, "和尚": 29078, "宝莲灯": 2373, "变形金刚": 29280, "谍": 20386, "谍海": 511, "神奇": 80552, "侠": 185206, "安生": 3006, "天幕": 1816, "杀机": 4557, "取景": 8161, "共分为": 3108, "新人奖": 5701, "合拍": 4625, "十六岁": 3749, "花季": 2614, "重播": 13019, "孽债": 390, "直指": 4372, "余留": 243, "面貌": 10447, "成片": 4154, "时装周": 4338, "创始于": 698, "大中华": 3164, "最优化": 2371, "兼备": 4084, "产业链": 9743, "主秀": 99, "首场": 6455, "担纲": 2860, "电视节": 3297, "旅游节": 995, "画展": 2990, "艺术展": 1466, "献艺": 375, "书展": 1939, "民居": 7746, "官署": 1283, "砖木": 1733, "平房": 13012, "老城厢": 217, "居民区": 4415, "新场": 1369, "别墅": 36543, "尤以": 5650, "分行": 46136, "摩天楼": 810, "沙逊": 318, "石库门": 746, "办公楼": 15587, "商场": 72953, "楼层": 15684, "几处": 4125, "旅游者": 3243, "A级": 12124, "景区": 44129, "东平": 4827, "共青": 1875, "海湾": 16153, "森林公园": 13416, "驰名中外": 696, "浦西": 1770, "摩天": 1886, "相隔": 10445, "成千上万": 7240, "商圈": 7124, "新天地": 7812, "地段": 32903, "五光十色": 1127, "绚丽": 5759, "十八号": 655, "和平饭店": 8720, "莎": 53415, "蜡像馆": 1307, "泰晤士": 1254, "滨江大道": 1287, "正大": 3502, "中国馆": 790, "常州": 16787, "徐光启": 874, "宋美龄": 2721, "宋霭龄": 250, "厉": 15972, "陈果夫": 325, "长居": 828, "李政道": 777, "锟": 1215, "文学界": 1190, "人才辈出": 872, "陶行知": 1060, "傅雷": 1187, "谷孙": 135, "曹禺": 1159, "邹韬奋": 339, "黄炎培": 548, "厉声": 875, "季羡林": 1552, "马寅初": 466, "梁实秋": 1045, "文化界": 1272, "胡歌": 19857, "孙俪": 20783, "黄圣依": 4345, "李俊": 3123, "唐嫣": 14550, "杨洋": 53050, "梓": 24677, "李修贤": 494, "张敏": 2382, "薛之谦": 18456, "尚雯婕": 2067, "体育界": 1326, "栏": 88307, "世界纪录": 9520, "保持者": 1799, "赛车手": 2865, "长江口": 832, "上海机场": 477, "文化教育": 3682, "王菊": 2700, "元生": 254, "道台": 676, "钻": 59610, "卢汉": 809, "炼": 25105, "霓虹灯": 1700, "张培": 825, "魏斐德": 49, "章红": 42, "芮": 10637, "歹": 1467, "恐怖活动": 1602, "正子": 499, "涛": 57515, "公共性": 525, "李欧": 545, "梵": 13386, "张旭东": 111, "意象": 6984, "消解": 2430, "玲玲": 3269, "李培德": 9, "地方志": 1459, "政协": 15497, "旅游局": 4662, "东方网": 927, "青年报": 352, "编撰者": 133, "某人": 23726, "某地": 6988, "刘知几": 209, "刘鹗": 300, "生动": 26931, "修饰": 19632, "牵涉到": 3241, "参加者": 7447, "新词": 3311, "时程": 1647, "集中式": 1268, "易货贸易": 77, "同侪": 857, "食谱": 16397, "侵权": 30665, "福特": 30279, "提姆": 2204, "中坚份子": 49, "渲染": 22224, "可乐": 23246, "葛林": 889, "穆迪": 1594, "比尔盖茨": 2308, "坐立难安": 199, "天才": 49461, "晓": 146710, "莺": 9066, "传讯": 2807, "文集": 7791, "矩阵": 29651, "特征值": 2744, "尔文": 470, "计算机系": 2258, "敬重": 3784, "字型": 7860, "发明人": 2198, "英文名": 14417, "枫": 33169, "简历": 48857, "威斯康辛州": 1219, "湖边": 6016, "密尔瓦基": 803, "水秀": 642, "贺": 122239, "美国人": 52369, "多才多艺": 2503, "星期天": 11347, "风琴": 1483, "印刷厂": 3757, "簿记": 510, "天分": 4608, "二年级": 14184, "糖果": 17823, "促销": 21278, "棒棒糖": 4051, "中办": 493, "疼": 136455, "两个星期": 6611, "裁判": 26790, "班级": 31654, "雪撬": 149, "各科": 4706, "高分": 20325, "犹豫": 27066, "克利夫兰": 3186, "凯斯": 3351, "读过": 20276, "主修": 5324, "赚得": 3429, "嘎": 21582, "出版商": 4262, "商定": 12996, "该书": 14066, "科学类": 926, "专论": 395, "安全局": 3439, "客座教授": 1748, "普拉特": 1320, "字符": 31540, "卡特": 62431, "颁与": 46, "荣获": 19203, "有趣": 91007, "美分": 5761, "进制": 7019, "酷爱": 4622, "管风琴": 1674, "吹": 110158, "萨克斯风": 964, "大号": 11970, "两度": 6624, "刊印": 659, "七十年代初": 170, "国家科学院": 1498, "富兰克林": 6715, "链": 58173, "箭": 47945, "表示法": 1550, "橙色": 15258, "标记为": 2595, "不脱": 2310, "汉地": 3088, "九州": 30502, "新石器": 815, "聚落": 9977, "代为": 34284, "君主制": 2785, "更迭": 2592, "场域": 731, "门前": 149346, "站住": 1806, "脚": 185684, "轻视": 6375, "国乐": 1235, "书法": 33204, "茶": 187638, "酒菜": 482, "筷子": 31288, "旧历": 1154, "元宵": 7459, "端午": 6635, "七夕": 12743, "中秋": 23955, "重阳": 10051, "冬至": 6673, "五伦": 358, "有教无类": 493, "因材施教": 2047, "察举": 474, "选拔": 20905, "历朝历代": 1269, "丰厚": 9545, "十一世纪": 524, "大邑": 977, "于天": 1242, "材": 60873, "皇天": 1031, "肆": 7474, "蛮夷": 1604, "臣属": 346, "有道": 8150, "大旗": 4522, "僭越": 452, "周天子": 1262, "差异性": 3411, "南唐": 3634, "闽南": 6146, "后蜀": 1084, "荆南": 1168, "更显": 5188, "前朝": 2735, "赐": 24243, "日本国": 2605, "朕": 8908, "耻": 6992, "辱": 7338, "振旅": 68, "番": 49742, "咨": 3950, "丰臣": 2437, "秀吉": 4890, "统治区": 299, "尼布楚": 569, "代称": 2289, "中土": 4986, "石刻": 5741, "帝舜": 261, "三千五百": 389, "二重": 3064, "证据法": 170, "四千二百": 84, "四千六百": 146, "八十万": 1221, "郧县": 1552, "北京人": 8660, "长阳": 3024, "观音洞": 242, "柳江": 1205, "资阳": 5223, "人等": 4206, "中石器时代": 186, "口耳相传": 775, "教民": 797, "渔猎": 859, "燧人": 124, "燧": 1618, "取火": 664, "治水": 2388, "中世": 839, "二十一世纪": 2933, "四百余": 375, "礼治": 297, "零七": 1639, "汉武": 3097, "隋末": 940, "唐高宗": 2768, "五十六年": 504, "渐衰": 232, "国威": 1695, "远播": 3983, "唐人": 5184, "燕山": 5780, "陈桥": 1113, "二百年": 813, "守势": 481, "割地": 1246, "蒙古人": 7246, "大中国": 1702, "农民起义": 3072, "转趋": 338, "虏": 6247, "遗臣": 316, "台澎": 176, "六百余": 213, "戊戌变法": 1721, "自救": 6592, "腐朽": 4628, "捍卫": 9202, "七月一日": 371, "一九二四年": 66, "卢沟桥事变": 834, "安理会": 6782, "国家化": 486, "抗美援朝": 4470, "连串": 952, "所有制": 2643, "经济制度": 1567, "禁运": 1861, "反扑": 1661, "九二": 1975, "第二十九届": 351, "升至": 6072, "七千年": 174, "中下游": 3976, "夏商周": 1320, "多国": 4734, "向西": 132612, "向北": 18416, "外兴安岭": 375, "压至": 294, "屡受": 301, "侵略": 18788, "数度": 1799, "地大物博": 825, "天然资源": 560, "江西省": 28679, "钨": 6961, "藏量": 130, "区划": 3658, "冀州": 6263, "青州": 4572, "雍州": 1895, "商周": 2523, "辽金时期": 89, "认知度": 1646, "西安": 97470, "郑州": 53264, "喜马拉雅": 4046, "欧亚": 6085, "阶梯状": 400, "昆仑山脉": 267, "祁连山脉": 111, "向东": 134777, "下降为": 518, "武陵山": 557, "雪峰山": 364, "大陆架": 1328, "浅海": 2395, "三分之二": 9120, "云贵高原": 1015, "黄土高原": 2585, "塔里木盆地": 1154, "准噶尔盆地": 512, "柴达木盆地": 665, "四川盆地": 1892, "东北平原": 756, "华北平原": 1232, "地形区": 339, "塔里木河": 705, "暖": 68672, "旱涝": 444, "害": 35799, "蝗祸": 14, "生计": 5741, "四十九": 2199, "均温": 558, "零度": 4699, "等温线": 513, "漠河": 1718, "海口市": 14662, "三十点六": 4, "十二点三": 25, "十二点九": 19, "三十三": 6185, "十九点六": 18, "诸郡": 435, "社稷": 3139, "统治权": 1511, "图存": 159, "子产": 1000, "邹忌": 520, "二二一年": 24, "嬴政": 3491, "二千": 4005, "宗法制": 481, "守成": 652, "人治": 1334, "分分": 1635, "一八": 2998, "四零": 966, "半殖民地": 1877, "有识之士": 1421, "一九一一年": 45, "一九四五年": 193, "后共产主义": 46, "一九七八年": 174, "军情": 3872, "巨鹿": 1641, "正规军": 2097, "数以十万计": 153, "四百万": 926, "八百": 6438, "兵书": 1486, "奉为": 4261, "兵家": 2342, "刘伯温": 1421, "弩": 9979, "三四二": 69, "马陵": 946, "魏军": 2090, "刺客": 27280, "荆轲": 4630, "九六": 1094, "一九六四年": 132, "十六日": 1654, "罗布泊": 1131, "原子弹": 10537, "雅克萨": 296, "彭春": 68, "三千": 18509, "俄军": 6081, "满洲": 8366, "越南战争": 2864, "远程": 60114, "十五时": 70, "一九六七年": 90, "十七日": 1457, "氢弹": 3498, "威力": 25769, "原爆": 684, "三百八十五": 250, "当量": 3743, "五百万": 2215, "黄色炸药": 182, "一千万": 4257, "达致": 942, "假想敌": 1161, "一九九六年": 281, "核试验": 4126, "一九八四年": 201, "生物武器": 725, "一九九七年": 569, "化外之地": 80, "南蛮": 2076, "无边无际": 1339, "所以说": 37270, "禄": 29545, "南掌": 185, "老挝": 9573, "属国": 1373, "十五世纪": 807, "混一": 340, "左下方": 2220, "绘有": 1932, "昭君": 2388, "汗血": 1024, "青睐": 15826, "苜蓿": 2635, "引进到": 452, "茶叶": 40627, "大唐": 27103, "仿制": 7630, "仿造": 2227, "样子": 138033, "打造": 66314, "吉备真备": 67, "王维": 7341, "李白": 33398, "好友": 99252, "亲手": 11629, "鉴真": 1002, "讲经": 1833, "播撒": 1117, "东瀛": 1345, "岛国": 5701, "做官": 3175, "天竺": 6260, "唐僧": 14331, "玄奘": 4468, "法师": 61735, "艰险": 1308, "翻山越岭": 966, "取到": 2738, "真经": 4452, "大乘": 7631, "仰慕": 4473, "三藏": 3756, "改编成": 5585, "西游记": 58601, "陆路": 3657, "不便": 18743, "造船业": 806, "罗盘": 3306, "马可波罗": 3588, "见识": 20941, "航海史": 130, "壮举": 2459, "三宝": 9464, "结实": 17289, "国策": 2423, "掠夺式": 128, "索马里": 6654, "欢喜": 14655, "利玛窦": 1194, "汤若望": 410, "南怀仁": 214, "威迫": 263, "望厦": 405, "中法": 4197, "密约": 572, "眼见": 3553, "租借地": 515, "不列颠": 6708, "德意志": 12720, "九零": 1974, "朴次茅斯": 392, "和约": 6270, "讲和": 1258, "战场": 53431, "一九零一年": 17, "战胜国": 1106, "沙皇俄国": 624, "缓付": 14, "五月底": 531, "不义": 2771, "奥匈帝国": 3281, "易帜": 654, "创始国": 222, "邦交国": 453, "一边倒": 3290, "一九五三年": 126, "日内瓦": 7005, "一九五五年": 220, "万隆": 1906, "亚非": 1960, "万隆会议": 274, "席间": 4252, "七十年代": 2868, "访美": 976, "八十年代": 6172, "一九九九年": 382, "二十日": 2836, "融洽": 7658, "一九八九年": 123, "天安门": 4998, "制裁": 13015, "军备": 3085, "交子": 1024, "世上": 32681, "占到": 5993, "侵华": 6387, "仿效": 2757, "赶超": 3074, "二零一": 4252, "六十多个": 165, "四十四亿": 6, "万亿美元": 3757, "六十三": 1011, "二十九个": 88, "巅峰": 27285, "环渤海": 1166, "珠三角": 6118, "经济区": 4700, "夏商": 1085, "海贝": 762, "墓中": 1326, "银质": 2313, "宋仁宗": 1821, "会子": 349, "通告": 10475, "熹": 3665, "石经": 554, "蒙语": 2100, "客家话": 2185, "音素": 1945, "书面语": 1942, "两千年": 2801, "笔谈": 457, "笔划": 2577, "两河流域": 1093, "苏美尔人": 500, "极少数": 8462, "拼写": 8139, "逾越": 3003, "字体": 63337, "大篆": 710, "隶书": 3246, "草书": 4284, "楷书": 6187, "行书": 4972, "极少": 14699, "契": 24810, "刻在": 3503, "龟甲": 1492, "兽骨": 596, "殷代": 146, "安阳小屯": 61, "造字": 2248, "写在": 20396, "延绵": 732, "藏文": 2747, "蒙文": 753, "满文": 1607, "西夏文": 354, "文房四宝": 799, "毛笔": 5723, "表现力": 4960, "书法家": 6998, "国画家": 330, "六种": 5347, "合璧": 797, "虞舜": 528, "官学": 621, "庠": 2029, "瞽": 636, "辟": 10280, "国子监": 2677, "闾": 4998, "乡校": 200, "闲时": 2082, "聚会": 36299, "得失": 7035, "被贬": 2040, "湖南省": 61210, "长沙市": 46649, "岳麓": 8596, "私": 57864, "义学": 619, "蒙学": 348, "均属": 10295, "官办": 814, "创设": 9123, "丽正": 205, "范仲淹": 2717, "都府": 187, "庆历": 1092, "盛极一时": 650, "这时候": 53812, "大儒": 1251, "程朱": 845, "供祀": 50, "两宋": 1101, "转衰": 543, "王阳明": 3020, "当道": 2557, "明世宗": 1120, "东林": 2447, "尽毁": 639, "没落": 8027, "改采": 750, "不复": 6789, "讲学": 3183, "改制为": 12646, "壬寅": 2047, "壬戌": 2785, "九年制": 465, "学龄儿童": 756, "均要": 1252, "珠海": 25076, "二年制": 615, "杂费": 1376, "高考": 152598, "高专": 2332, "欠佳": 5998, "三年级": 17251, "考研": 102342, "存世": 2261, "真迹": 1520, "阳台": 43579, "释文": 576, "山高水长": 352, "物象": 1133, "壮": 28319, "十八日": 1478, "章回小说": 537, "论语": 11763, "唐初": 1468, "隋书": 1432, "经籍": 723, "六万九千": 73, "五百七十": 91, "千三百": 53, "二十余册": 9, "零八": 2732, "二百余": 630, "志道": 625, "志书": 369, "画": 216080, "自古以来": 8323, "从政者": 202, "一向": 21977, "公刘": 315, "孔雀东南飞": 1205, "木兰": 13916, "难得": 31296, "取材": 5381, "维持到": 757, "集合体": 1582, "水经注": 1273, "吕氏春秋": 1578, "淮南子": 1405, "格萨尔": 662, "江格尔": 184, "玛纳斯": 1314, "创世": 4734, "五十五": 3056, "天一阁": 705, "刻本": 2023, "四书": 2111, "飞跃": 9750, "韩非子": 1880, "自成一体": 525, "精华": 38430, "深深地": 7840, "精神焕发": 554, "嬗变": 693, "神主": 722, "前古": 271, "民意": 8542, "化身": 14798, "至圣先师": 373, "昊天": 1714, "子民": 2556, "降下": 3053, "决": 32940, "正定": 2858, "公羊": 2678, "仪礼": 914, "古文": 8688, "篆": 4334, "隋唐时代": 156, "礼教": 1344, "四书五经": 1438, "冠": 69705, "昏": 14768, "丧祭": 42, "丧礼": 1523, "祭礼": 660, "纵横家": 778, "佛学": 5922, "宇宙观": 849, "老庄": 3250, "社会观": 227, "孔孟": 1456, "轮回": 19228, "缘": 88796, "唯心": 1748, "孔孟之道": 316, "修养": 20384, "中心思想": 3265, "仁义": 5260, "仁政": 951, "官民": 897, "从父": 591, "大行": 2947, "专才": 733, "通才": 772, "君子": 24000, "适": 28844, "成德": 1386, "无不": 9486, "艺": 68355, "重义": 1182, "待价而沽": 252, "子贡": 1459, "赎": 5188, "鲁人": 527, "批驳": 1158, "重理": 214, "形上学": 295, "太极": 24056, "两仪": 1191, "吉凶": 3443, "无方": 652, "所见": 12798, "述而不作": 118, "老彭": 188, "述而": 292, "道德家": 142, "隐士": 3434, "达观": 826, "厌世": 1573, "道德经": 4471, "相处": 69892, "认": 52324, "无所作为": 820, "养息": 113, "向往": 31289, "反璞归真": 80, "朴实": 8613, "无虑": 421, "烦恼": 49855, "解脱": 14103, "人生观": 8129, "清虚": 1076, "自守": 928, "自持": 1869, "安身立命": 927, "治国安邦": 203, "心情": 150890, "区别性": 157, "有性": 4713, "有心": 11222, "有情": 11900, "自在": 14599, "无可奈何": 5224, "首倡": 415, "守静": 142, "坐忘": 457, "提升为": 3056, "标举": 124, "道法": 1911, "冶": 13949, "利于": 14890, "生息": 1883, "玄虚": 403, "竞尚": 7, "宋文帝": 1028, "旨令": 142, "彦": 49191, "星图": 1271, "星座": 64035, "黑黄": 816, "标注": 26424, "配以": 6280, "哲学系": 2970, "蓝本": 4337, "发明创造": 2157, "四大发明": 1495, "指南针": 3945, "地动仪": 714, "伞": 29851, "张开": 11798, "火柴": 11137, "漆器": 1827, "风筝": 21492, "热气球": 2064, "连弩": 624, "独具特色": 2162, "科学史家": 109, "李约瑟": 774, "普尔": 7966, "领先于": 1700, "十进制": 4353, "一百二十余": 105, "次于": 3238, "一九一零年": 19, "举世公认": 322, "太阳黑子": 1453, "五行志": 221, "河平": 121, "负数": 7412, "刘徽": 580, "小数": 12653, "祖冲之": 1291, "圆周率": 2894, "计算至": 682, "测算": 9172, "简易": 28396, "八十": 11151, "磨坊": 1960, "航海术": 59, "隙": 7177, "谈到": 24858, "磁针": 674, "梦溪笔谈": 768, "八零年": 53, "郭守敬": 432, "授时历": 133, "延用": 828, "匠师": 484, "黄履庄": 17, "晓庵新法": 9, "五星": 20132, "王锡阐": 30, "中体": 610, "拥护者": 1800, "冯桂芬": 85, "名教": 575, "发动机": 168448, "耆英": 529, "好望角": 1555, "面海": 346, "帆船": 7877, "南州": 1312, "风帆": 3907, "迎风": 4146, "倾斜": 20457, "迎风面": 152, "牲": 2273, "些微": 1382, "航速": 4215, "逆风": 4349, "帆": 29818, "竹竿": 5695, "篷帐": 19, "船长": 15002, "十米": 2312, "七百": 3129, "重量": 79752, "因地制宜": 2569, "船身": 1188, "各异": 6614, "有别": 2974, "丹吉尔": 295, "旅行家": 1405, "泉州": 23230, "湾": 123859, "十一点四": 21, "三点三": 116, "十三个": 1664, "船舱": 1724, "隔板": 2840, "厚达": 788, "厘米": 119671, "梁用": 114, "三四": 15004, "木板": 15014, "榫": 1581, "接而": 283, "肋骨": 40114, "舱壁": 345, "铁钉": 1780, "加固": 10869, "船板": 239, "缝隙": 15895, "捻": 4828, "海员": 2728, "安息": 5054, "波斯湾": 2866, "斯里兰卡": 8835, "万里": 30676, "近海": 5472, "航行": 16853, "通达": 7603, "贾耽": 71, "通海": 3275, "写到": 13623, "狮子国": 94, "续航": 23801, "阿拉伯海": 851, "东北岸": 270, "驶达": 30, "波拉": 4264, "萍": 23704, "舟师": 282, "观星": 1241, "昼": 11859, "观日": 307, "阴晦": 167, "二百四十多": 28, "海船": 641, "马达加斯加": 6552, "三十余": 1719, "迪亚士": 229, "美洲大陆": 1115, "八十年": 978, "远航": 2748, "七万多": 264, "圆周": 4887, "三倍": 11038, "戎克船": 55, "排水量": 5991, "小船": 7209, "防水": 53707, "舱": 12057, "运载": 5978, "货品": 6597, "王冠": 6462, "古船": 564, "图谱": 4865, "自愈": 7274, "炎黄": 2074, "大漠": 6375, "对半分": 457, "景教": 777, "超前": 7005, "时而": 12729, "循环不断": 62, "宋夏": 204, "推翻满清": 163, "立宪派": 375, "革命派": 927, "翦伯赞": 346, "陕": 46323, "云贵": 1619, "川渝": 1341, "宁夏": 29277, "哈萨克族": 1891, "总计": 26845, "三千五百万": 111, "五分之一": 3923, "节育": 2317, "降低到": 3623, "后面": 222865, "二胎": 11483, "开放为": 237, "放开": 24481, "路上": 126778, "悠长": 2781, "排在": 17583, "以农立国": 46, "吃饭": 232609, "第二层": 4889, "工匠": 10538, "刀剑": 10681, "第三层": 3015, "狡诈": 1908, "阴险": 3336, "第四层": 1193, "当兵": 17793, "森严": 1445, "深入人心": 4958, "官本位": 709, "浓厚": 15827, "读书人": 4072, "儒家经典": 919, "三纲五常": 427, "人伦": 1834, "孝道": 2452, "不孝有三": 413, "无后为大": 416, "娶为": 82, "无后": 1152, "娶妻": 2843, "自由恋爱": 1313, "父母之命": 515, "媒妁之言": 338, "父权": 1425, "纳妾": 748, "上流": 5509, "初具": 303, "严刑": 963, "抵罪": 143, 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"候机": 905, "被告": 87874, "晚点": 6370, "接到": 96249, "检修": 13661, "登机": 6104, "旅行团": 2686, "道歉": 42134, "回家": 273682, "数名": 3014, "闹事": 10539, "记入": 4769, "日据": 2046, "原计划": 4562, "喜悦": 16237, "一旁": 14135, "高声": 3568, "叫好": 4536, "推搡": 11918, "全额": 16559, "退款": 27389, "保险公司": 51226, "赔付": 17063, "纪念物": 897, "大连路": 1083, "地铁站": 27031, "号口": 2022, "拍摄地": 1416, "开馆": 2735, "该馆": 2010, "传唱": 3750, "童声": 1704, "半个多世纪": 977, "人民网": 11451, "浮沉": 2537, "中国人大": 406, "断代": 1242, "繁": 21837, "处所": 3612, "夏代": 846, "祖庚": 75, "祖甲": 84, "廪": 1134, "辛康": 44, "高祖": 3981, "鸿门": 300, "宴": 20378, "乌江": 3388, "自尽": 4210, "韩信": 10745, "彭越": 692, "雉": 7399, "黥布": 161, "刘义": 1747, "贾谊": 1187, "吴楚": 1250, "朔": 11324, "元狩": 497, "连山": 2606, "李广": 2203, "司马相如": 1125, "鼎": 41000, "嫁": 53867, "猎": 49397, "骄": 7533, "靡": 5208, "太初": 1456, "李陵": 1262, "宫刑": 555, "太始": 154, "东方朔": 943, "征和": 312, "李广利": 352, "陵前": 256, "苏武": 1870, "桑弘羊": 233, "元平": 334, "刘询": 280, "梨": 47466, "龟兹": 1466, "数万": 5924, "出迎": 245, "甘露": 4568, "黄龙": 8544, "爽": 75020, "初元": 209, "永光": 985, "王昭君": 3782, "王嫱": 91, "建始": 547, "阳朔": 3512, "鸿嘉": 174, "永始": 124, "中元": 1783, "明帝": 2220, "刘庄": 2111, "永平": 4389, "愔": 845, "佛典": 642, "僧": 34577, "白马寺": 1222, "建初": 487, "狱中": 5282, "王充": 681, "蔡伦": 1362, "延平": 2419, "元初": 744, "永宁": 7280, "北乡": 674, "永建": 480, "永和": 8844, "张衡": 2359, "建和": 876, "安世高": 228, "永兴": 8387, "永寿": 1634, "月氏": 873, "娄": 29143, "谶": 952, "建宁": 4366, "中平": 1717, "献帝": 1046, "兴平": 1580, "自重": 6144, "郑玄": 868, "三顾茅庐": 1374, "鲁肃": 2353, "吕蒙": 2368, "章武": 507, "张飞": 11300, "建兴": 2852, "后主": 4683, "魏明帝": 596, "叡": 2995, "太和": 10214, "马谡": 1313, "嘉禾": 7606, "青龙": 17313, "熙": 38614, "赤乌": 406, "齐王": 2764, "嘉平": 580, "王孙": 2718, "高贵": 15626, "髦": 1280, "竹林七贤": 581, "嵇康": 1609, "孙皓": 1401, "宝鼎": 1053, "天玺": 364, "天纪": 302, "大人物": 3611, "个性化": 20110, "处事": 10495, "家教": 14411, "教育权": 683, "子宫": 65998, "字汇": 882, "作准备": 1696, "会意": 1887, "手持": 42939, "杖": 18937, "执鞭": 243, "头上": 41003, "引导性": 521, "日程表": 1320, "程序化": 1695, "心智": 9994, "教学大纲": 1751, "知识点": 21706, "学前教育": 7005, "学龄": 1007, "幼教": 5127, "同龄": 6696, "受教育者": 820, "五岁": 8610, "性教育": 4355, "中等教育": 1608, "新西兰": 28031, "当做": 30209, "义务性": 174, "国别": 1258, "七年级": 3282, "十二年级": 192, "岗位": 78177, "更好地": 22730, "升任": 10965, "白领工人": 41, "蓝领工人": 234, "益处": 7388, "专业性": 9779, "相伴": 10573, "手工艺": 1983, "家长会": 3739, "几百万年": 664, "也可以说": 10100, "哈雷": 3119, "区别开来": 1040, "臻于": 447, "舍人": 3043, "耒耜": 263, "砚": 9089, "纪胜": 302, "剑": 198725, "乐教": 468, "塾": 2415, "暮": 25578, "洋洋": 3556, "宗学": 205, "家塾": 370, "书会": 1592, "里巷": 536, "相闻": 500, "叶适": 212, "闽": 84621, "著书": 1977, "日文": 19605, "文盲": 3923, "教育基金": 971, "国三": 1526, "打伤": 104772, "落在": 17917, "萨瓦": 2247, "拉达": 3022, "克里希": 948, "杰尔": 2677, "布卢姆": 725, "夸美纽斯": 239, "奥本": 817, "福禄": 5113, "加涅": 158, "赫尔巴特": 215, "伊里奇": 487, "马利亚": 3144, "蒙特梭利": 706, "西摩尔": 210, "普特": 2423, "裴斯泰洛齐": 99, "波兹": 1430, "艾米": 10228, "利亚": 33278, "瑞吉": 1448, "斯金纳": 605, "俄语": 15595, "彼得森": 865, "李家同": 80, "钱思亮": 92, "行政学": 609, "工学": 6361, "卫生学": 494, "托儿所": 2150, "国民中学": 4215, "培训班": 19405, "幼儿教育": 3758, "终身教育": 619, "成人教育": 3645, "函授": 5211, "工读": 307, "家庭教育": 8750, "亲职教育": 82, "特殊教育": 2686, "资优": 557, "品格": 11738, "补习": 8289, "素质教育": 8590, "奴化": 382, "教学法": 3094, "发现式": 43, "课堂": 57624, "列明": 2498, "才智": 2565, "联合国宪章": 1010, "原籍国": 48, "本着": 10352, "土著居民": 781, "男女平等": 3856, "一九五零": 71, "外语": 31670, "瞬间": 67673, "窥探": 2128, "早先": 3680, "作图": 3828, "机器翻译": 1090, "起始": 11494, "海斯": 2022, "句法": 2980, "语用学": 425, "门径": 527, "划分成": 1623, "区块": 11743, "遭逢": 987, "检定": 4857, "记忆力": 19510, "要件": 7728, "语料库": 1004, "最常": 6870, "宾大": 485, "四百五十万": 52, "英文字": 984, "词类": 1035, "括": 5061, "内化": 1451, "简单化": 2048, "茁壮": 1330, "语句": 28696, "流利": 8678, "另一半": 15507, "两场": 13869, "陈述": 631065, "罗杰斯": 3207, "治疗师": 2771, "看似": 34227, "句型": 5616, "认得": 4328, "关键字": 12074, "转成": 10021, "打入": 18890, "词句": 4266, "据以": 9571, "音波": 912, "随处可见": 5241, "令人振奋": 911, "客服": 102930, "贝氏": 1089, "联邦党": 544, "还算": 36435, 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"大部": 7342, "偏多": 2133, "辖市": 295, "县级市": 5734, "自治县": 73341, "鞍山市": 9588, "抚顺市": 4106, "本溪市": 5398, "锦州市": 5292, "辽阳市": 4657, "营口市": 3439, "阜新市": 4037, "盘锦市": 4886, "铁岭市": 3282, "葫芦岛市": 3931, "瓦房店市": 3691, "庄河市": 3333, "海城市": 5872, "东港市": 2541, "凤城市": 1473, "凌海市": 1583, "灯塔市": 1221, "盖州市": 2034, "大石桥市": 2081, "市调": 379, "山市": 6017, "北票市": 1416, "兴城市": 2911, "正增长": 240, "周岁": 69559, "第六位": 1626, "各市": 1593, "锡伯族": 808, "杂居": 801, "阜新": 4425, "喀": 9978, "聚居区": 1516, "公教": 900, "玉佛": 1168, "人参": 19934, "细辛": 1491, "五味子": 2805, "党参": 4780, "天麻": 4024, "野果": 1307, "山葡萄": 298, "猕猴桃": 10679, "山里红": 2129, "山梨": 1909, "芳香油": 438, "藿香": 2236, "薄荷": 13992, "蔷薇": 21010, "油脂类": 872, "松子": 4868, "苍耳": 664, "野菜": 4724, "染料": 10010, "哺乳": 13863, "爬行": 6171, "白鹳": 188, "丹顶鹤": 1175, "蝮蛇": 1453, "爪": 29557, "鲵": 955, "赤狐": 269, "海豹": 6353, "齐全": 30790, "矿种": 390, "页岩": 3024, "钼": 5517, "熔": 17016, "灰岩": 1019, "滑石": 1374, "菱": 17432, "镁": 21260, "金刚石": 5015, "黑色金属": 798, "有色金属": 3968, "浮游生物": 1952, 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23540, "万件": 1660, "大省": 2397, "省内": 11037, "鞍钢": 1091, "本钢": 461, "大厂": 7054, "遍地开花": 1181, "中小": 15953, "钢厂": 3327, "特钢": 1225, "乙烯": 6914, "轮胎": 55434, "条列": 386, "树脂": 19764, "共聚": 2798, "强省": 615, "总产量": 2118, "丰收年": 278, "四通八达": 1340, "交通网": 743, "专线": 23060, "桃仙": 740, "锦承": 133, "干支线": 115, "通盘": 602, "城际": 9161, "京沈": 1246, "周水子": 274, "腾": 67203, "鳌": 5887, "合用": 5055, "支线": 19092, "浪头": 529, "小岭": 572, "东塔": 640, "全胜": 3246, "苏家屯": 3198, "长海": 944, "沈大": 643, "第一条": 15870, "全线": 17925, "双向": 16889, "车道": 47129, "大中城市": 2137, "鲅鱼": 1797, "动脉": 23735, "水运": 2389, "羊头": 2346, "张三丰": 3490, "祖大寿": 277, "耿": 46248, "仲明": 446, "范文": 6787, "李希烈": 222, "有德": 2087, "药师": 11794, "盛世才": 817, "颇具": 5528, "京剧院": 311, "京剧院团": 23, "麒": 5970, "北马": 1161, "表演艺术家": 1645, "筱": 8942, "少云": 896, "淑兰": 301, "花派": 30, "俊": 71920, "民间艺术团": 176, "王志": 2350, "央视": 48239, "夺魁": 774, "阔成": 198, "刘兰芳": 444, "赵本山": 14732, "黄宏": 1128, "巩": 14029, "潘长江": 6577, "娟": 19678, "芭蕾舞团": 1028, "辽菜": 47, "山珍": 824, "野味": 1573, "技法": 9381, "扒": 25587, "煎": 55200, "炒": 125800, "烹": 8587, "见长": 3341, "工艺品": 10038, "岫岩玉": 182, "玛瑙": 7607, "民风": 3132, "大秧歌": 2811, "闲散": 2318, "婚俗": 760, "七天": 15926, "一宿": 2322, "新娘子": 1374, "拜完": 175, "房门": 28127, "几辈": 223, "健在": 2863, "全科": 3372, "漂亮": 179888, "小姑娘": 23067, "媳妇": 42441, "递来": 284, "酒壶": 1003, "宝贝": 117771, "新房": 19731, "窗台": 7564, "回娘家": 4062, "叫回": 391, "娘家": 11014, "婆家": 4600, "婚娶": 284, "旧俗": 453, "如若": 6624, "合婚": 1040, "媒人": 2515, "相看": 2923, "相亲": 40726, "中意": 8447, "定婚": 442, "抓": 111349, "拐": 13375, "猪羊": 669, "抓来": 1085, "抛去": 1162, "做得": 44076, "针线活": 304, "院内": 46511, "门牌号": 1175, "无线电台": 981, "址": 10672, "辽沈晚报": 134, "商报": 3254, "晚报": 5863, "第一时间": 23197, "昭陵": 1328, "金石滩": 990, "旅顺口": 579, "水洞": 1296, "山沟": 2569, "山红": 782, "苇": 6413, "望儿": 217, "仙人": 10575, "大青山": 1312, "赤山": 716, "沈阳故宫": 531, "豪华": 33600, "东陵": 2351, "北陵": 676, "星海": 4184, "欣赏到": 1511, "华表": 1159, "拆除": 35547, "音乐喷泉": 1134, "游艺": 2028, "木质": 17336, "内饰": 13973, "棒棰岛": 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"年满": 14616, "二十岁": 6824, "行为能力": 10013, "未成年": 15100, "跨出": 1284, "国境": 3495, "居住国": 238, "住在国": 21, "居留权": 1477, "自费": 8161, "外派": 1838, "劳务": 30100, "所生": 8892, "布鲁克林": 3915, "海路": 6865, "留居": 488, "他乡": 4773, "跨洋": 270, "红海": 11132, "移民至": 567, "做生意": 25756, "明乡": 238, "殖民主义": 1462, "移民潮": 680, "生意": 78499, "可耕地": 389, "染上": 6645, "瘾": 11486, "此际": 561, "远赴": 2970, "穷人": 22383, "乞丐": 10442, "上百万": 3742, "前辈": 29109, "广东台": 664, "澳纽": 175, "卖到": 8766, "墨西哥": 36737, "大洋洲": 6540, "苦力": 2816, "猪仔": 1746, "最苦": 1319, "被骗": 41155, "到期": 46975, "衣锦还乡": 508, "城楼": 2460, "归乡": 1655, "洽询": 135, "回流": 7822, "南侨": 835, "南洋群岛": 252, "而今": 6199, "五千万": 1207, "移民地": 60, "二十名": 627, "侨务": 1221, "美加": 2246, "克勤克俭": 175, "钜": 2832, "五一三": 239, "马来人": 2445, "本地人": 9594, "送去": 10842, "招标": 30768, "年方": 1348, "解禁": 4176, "岐视": 162, "以国": 1119, "泰国人": 1710, "高棉人": 203, "外国语文": 249, "懂得": 70302, "印尼语": 1222, "越南人": 2884, "一水之隔": 115, "老一辈": 5720, "越战": 5640, "未返": 282, "餐饮业": 3087, "部会": 2543, "财力": 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"不曾": 15149, "乔治亚": 3100, "奥塞提亚": 103, "起因于": 697, "车诺比": 217, "针锋相对": 1910, "拉夫罗夫": 444, "奉还": 743, "登岛": 1239, "山口组": 2018, "上院": 1150, "下院": 1696, "杜马": 1087, "库兹涅佐夫": 1088, "红场": 1219, "红四军": 886, "红一": 1411, "副司令": 2083, "十一月初": 1672, "仪陇县": 1710, "考中": 3182, "跋涉": 1682, "司务长": 224, "教官": 8788, "营长": 3274, "个旧": 1083, "支队长": 1420, "旅长": 4099, "少将": 23475, "每周评论": 107, "书刊": 3080, "下任": 1091, "宪兵": 3949, "变乱": 540, "十万元": 4352, "军饷": 1105, "相许": 1190, "婉拒": 3182, "吊销": 15923, "邓演达": 253, "旧有": 2145, "泸": 4758, "老同学": 2957, "拨出": 2568, "副军长": 1613, "蒋光鼐": 331, "蔡廷锴": 436, "南进": 778, "暑热": 1253, "沿途": 10328, "三河坝": 89, "进击": 5542, "坚守": 13172, "天心": 3051, "组织纪律": 727, "统战": 1807, "党团员": 80, "连队": 4903, "范石生": 128, "王楷": 69, "粤赣边": 123, "宜章县": 2797, "工农革命军": 345, "游击战": 2512, "运动战": 695, "打垮": 1147, "许克祥": 64, "独立团": 1136, "顾作霖": 47, "任弼时": 697, "邓发": 174, "龙源口": 59, "退敌": 196, "扰": 26513, "疲": 5371, "字诀": 604, "朱毛": 295, "免去": 6015, "前委": 527, "古田": 4226, "红六军": 82, "红三军": 220, "长汀": 1699, "红三军团": 386, "唯物": 1160, "章法": 1979, "中央红军": 1045, "红四方面军": 1292, "左路": 1677, "红二方面军": 288, "洛川": 1034, "华北军": 21, "晋升": 16870, "八路军总部": 358, "平型关": 1267, "晋西北": 396, "晋冀豫": 114, "上左": 377, "冀南": 858, "朱怀冰": 39, "左权": 1133, "百团大战": 1318, "南泥湾": 1787, "大生产运动": 102, "七中全会": 153, "大作": 10024, "大反攻": 489, "及早": 10549, "编组": 11151, "野战": 5924, "清风店": 134, "设防": 3045, "攻坚战": 1485, "动员报告": 21, "长辛店": 628, "检阅": 2706, "正规化": 1318, "既要": 9196, "平战": 213, "紫光阁": 384, "官僚主义": 1804, "面前": 107398, "一视同仁": 2373, "日向": 31039, "纪检": 7194, "民主生活": 58, "遵纪守法": 3287, "饶漱石": 282, "致函": 1382, "抓紧": 17714, "发言": 28132, "勤俭持家": 698, "痒处": 367, "强求": 8471, "公共食堂": 99, "集市贸易": 110, "得过": 7950, "社员": 5076, "家庭副业": 40, "多种经营": 517, "门路": 5556, "五一六": 208, "十一中全会": 108, "搞到": 6102, "授意": 6237, "造反派": 1094, "勒令": 2749, "十二中全会": 65, "广东从化": 75, "二中全会": 474, "马尔科姆": 1424, "并发症": 20106, "追悼": 1109, "悼词": 697, "火化": 9858, "骨灰盒": 1376, "存于": 2874, "康克清": 162, "雅号": 635, "老总": 11715, "大节": 987, "六十岁": 1999, "分不开": 3886, "嘛": 209393, "当过": 9187, "张春桥": 586, "黄袍加身": 367, "第一天": 33976, "谛听": 523, "置身": 5612, "军内": 349, "军纪": 1285, "机务段": 3993, "配属": 3308, "世林": 438, "生母": 7325, "钟氏": 358, "过继": 1079, "语文课": 5285, "作主": 1508, "大舅": 3730, "许配给": 806, "完婚": 1678, "守空房": 305, "嫡母": 969, "送葬": 1620, "赤痢": 92, "挚友": 3759, "介绍给": 9099, "暂居": 766, "家兴": 390, "何键": 329, "未及": 2480, "被抓": 15018, "寻到": 798, "中央党校": 2682, "腿部": 25343, "中弹": 2442, "致残": 5809, "调回": 2646, "队列": 9159, "转业": 4980, "练习生": 10584, "援朝": 481, "全华": 120, "新华": 27640, "干休所": 709, "门诊部": 4073, "朱国华": 40, "严打": 1790, "教研室": 2516, "离休": 3211, "军地": 569, "两用人才": 51, "刘敏": 933, "大队长": 4711, "输入法": 31522, "检字": 9258, "内码": 714, "蒋纬国": 550, "登报": 2613, "中文化": 921, "形码": 291, "打字机": 2076, "沈红莲": 14, "分拆": 4148, "无给职": 90, "减省": 268, "内置": 29401, "天龙": 10667, "甚高": 1528, "汉卡": 226, "该机": 8143, "售价": 32318, "更低": 13826, "选取": 20469, "四万多": 589, "音义": 600, "取码": 174, "字义": 2868, "异体": 3159, "最低者": 132, "字库": 2101, "整套": 5580, "产生器": 523, "优缺点": 11600, "看着": 175363, "稿子": 4773, "打字": 24112, "紧盯": 1231, "萤光幕": 249, "四排": 905, "按键": 37507, "数字键": 1936, "中文字": 1193, "移位": 6222, "拆分": 7339, "容错": 2225, "略显": 6952, "畏途": 283, "常用字": 984, "五笔": 8434, "词组": 6432, "标点符号": 3914, "略嫌": 219, "优胜者": 3398, "笔画": 14643, "笔顺": 2713, "剪开": 3606, "歧途": 1088, "构形": 481, "分析会": 420, "剪裁": 5051, "呒": 641, "文宣": 1250, "苍颉": 113, "坊间": 3542, "第六代": 2701, "框": 55314, "速成": 5411, "重重": 13297, "一万二千": 578, "略作": 841, "简体字": 3052, "资策会": 145, "有错": 8988, "苍": 42738, "交换码": 144, "内附": 1570, "氵": 1155, "丨": 17282, "示例": 12985, "分则": 1183, "字码": 517, "如东": 1073, "空白键": 106, "拆字": 624, "熟习": 422, "全形": 199, "弹出": 59603, "三百名": 201, "打字员": 1274, "分词": 7329, "千方百计": 4086, "找回": 45132, "字字": 2270, "重码字": 28, "华通": 1406, "首尾": 2465, "更正": 8542, "词库": 2170, "倚天": 2926, "中标": 20658, "主计处": 377, "七万": 2994, "国字": 603, "一弓": 205, "泰瑞": 2203, "表意文字": 560, "并做": 2654, "奇摩": 937, "常用词": 601, "新手": 76524, "老手们": 107, "词频": 451, "简化字": 2296, "国家语委": 195, "征求意见稿": 1885, "录入": 11794, "宋体字": 338, "一二级": 597, "简码": 411, "六段": 1441, "全方位": 15205, "鲸鱼": 8489, "初版": 4259, "设计成": 4913, "地表达": 3343, "老手": 4320, "解译": 399, "转换到": 3851, "不成文": 1406, "开关": 98647, "账号": 127187, "低端": 15183, "译作": 6325, "表达式": 14196, "常量": 3963, "指针": 17737, "宏调": 186, "转档": 169, "烧录": 1677, "上千种": 468, "精准度": 1665, "交织": 6570, "基元": 569, "征才": 87, "可视": 7100, "脑细胞": 4873, "轻敲": 683, "代码库": 615, "信息图": 416, "汇为": 131, "祸及": 412, "遗族": 600, "当政": 1549, "泰始": 1074, "初建": 767, "二代": 23563, "二十七个": 139, "获授": 1159, "大郡": 166, "自选": 5667, "授职": 400, "荀": 7294, "攸": 4561, "衷": 3307, "八位": 2719, "杨骏": 370, "虚线": 3182, "青色": 5638, "重病": 5094, "借出": 2329, "看后": 5492, "中书": 5232, "索取": 19681, "中书令": 1681, "何劭": 40, "旨": 7499, "拿给": 7551, "愚钝": 1129, "贾充": 624, "相貌": 10588, "凶狠": 2895, "诈": 6064, "安插": 1534, "过目": 850, "交予": 4930, "碍": 3096, "禁军": 1624, "不以为然": 3687, "劝止": 1040, "不纳": 522, "东宫": 3129, "黄门": 1299, "殿中": 1677, "交结": 512, "楚王": 3780, "请入": 193, "看守": 13199, "应允": 2012, "废黜": 2071, "驻屯": 1094, "云龙门": 28, "繇": 1361, "宫中": 7220, "有变": 2029, "属官": 650, "主簿": 2943, "看穿": 5557, "谋划": 4132, "祸首": 538, "入宫": 3290, "震慑": 3216, "速速": 1468, "主谋": 1269, "怯懦": 1911, "侍中": 4801, "祗": 3369, "自请": 568, "察看": 4302, "作揖": 722, "并放": 636, "杨府": 366, "顾命": 523, "手诏": 145, "布帛": 412, "太傅": 3187, "有赏": 265, "救到": 196, "囚禁": 6995, "瓘": 1057, "分任": 261, "太宰": 1449, "王玮": 269, "北军": 1424, "狠": 26029, "宫禁": 317, "记恨": 1371, "不端": 1499, "讨厌": 88495, "祸胎": 107, "收捕": 277, "假托": 534, "中伤": 2053, "乘势": 739, "图谋": 2095, "废立": 595, "授命": 293, "假传": 293, "迎击": 1676, "被擒": 1129, "遐": 3136, "晦": 4727, "独揽": 805, "尽忠": 891, "散去": 3050, "失措": 314, "廷尉": 1014, "图谋不轨": 510, "谧": 946, "贾氏": 566, "王伦": 772, "遹": 531, "才人": 1397, "玖": 12457, "妹夫": 4410, "韩寿": 162, "王衍": 761, "大感": 870, "礼敬": 514, "临终": 5955, "姨甥": 76, "灌醉": 1754, "言辞": 7391, "悖逆": 433, "囚": 11649, "押到": 827, "都得": 18963, "赵王": 3069, "求援": 2257, "改向": 472, "反间计": 580, "流言": 5072, "果然": 41325, "害怕": 107386, "中计": 1074, "虑": 6961, "宫门": 1909, "陈兵": 510, "内应": 1826, "毒杀": 1903, "贾午": 80, "使持节": 1461, "事事": 12371, "大权在握": 344, "滥权": 467, "石崇": 566, "潘岳": 614, "京中": 299, "淮南王": 728, "蓄养": 249, "死士": 676, "军心": 1446, "愤而": 1564, "帐下": 1078, "屡败": 351, "逼得": 3444, "躲在": 13740, "狼狈": 3274, "假称": 1582, "骗得": 42360, "下车": 103932, "假作": 263, "登位": 582, "收买": 4824, "官爵": 808, "府库": 439, "绶": 3362, "衔": 12881, "颙": 1252, "强兵": 458, "忌惮": 1856, "僚属": 304, "共谋": 10543, "司管": 90, "献给": 14730, "谋定": 121, "骧": 2565, "常山": 5087, "新野": 5192, "召见": 2557, "邺令": 45, "贤才": 763, "王彦": 1055, "二十多万": 630, "押": 18692, "追上": 14867, "张泓": 74, "路经": 3868, "成皋": 400, "齐军": 806, "黄桥": 2775, "辎重": 1265, "赵军": 1378, "收兵": 628, "超等": 822, "初战": 391, "退还": 83036, "轻敌": 1133, "王遂": 132, "无计可施": 924, "下一步": 45836, "左将军": 770, "党羽": 1307, "兵将": 576, "刑讯": 374, "撤换": 1392, "王军": 2044, "王都": 1549, "推给": 2659, "拜谒": 681, "称誉": 610, "败亡": 1053, "掌政": 356, "大兴土木": 798, "沉迷": 13459, "逸乐": 194, "用人唯亲": 55, "谏臣": 152, "失望": 62347, "归心": 1696, "翊": 5537, "皇甫": 3312, "只身": 2883, "密诏": 222, "献计": 1361, "遂于": 7212, "上表": 2513, "惊惧": 686, "王戎": 517, "让位": 1176, "退避": 525, "枭首": 370, "曝尸": 98, "大出": 626, "河南尹": 391, "荪": 1407, "卞": 16514, "王颖": 471, "遥控": 21485, "精兵": 2003, "攻向": 347, "亲率": 1272, "拒": 16503, "攻至": 289, "干戈": 1898, "不允": 688, "六七万": 332, "坚毅": 3132, "抗敌": 1139, "王越": 378, "撑不下去": 718, "愤恨": 2062, "尚书令": 1634, "留守": 10943, "乘舆": 274, "夔": 2643, "五万": 7551, "骄奢": 309, "乱民": 126, "恃": 2829, "废弛": 311, "扰乱": 14650, "弛": 2401, "甚于": 1617, "如故": 2018, "魏武": 1214, "相府": 726, "王官": 453, "侈": 1083, "日甚": 108, "大失": 384, "众望": 353, "荡": 12902, "旧将": 672, "十万多": 282, "归罪": 228, "陈规": 452, "松懈": 3198, "落得": 2594, "百官": 3750, "生杀": 319, "郊祀": 423, "守住": 5389, "伺机": 22951, "天雨": 984, "攻进": 889, "抢掠": 879, "五日": 8204, "掳掠": 693, "营垒": 461, "归国": 5478, "方强": 159, "胁": 4097, "愤": 7637, "楙": 214, "盟主": 3051, "东归": 1456, "朝臣": 1603, "公师": 66, "汲桑": 69, "思念": 30909, "晞": 2419, "借兵": 380, "王东平": 14, "怯": 5407, "溃散": 921, "怯于": 221, "北进": 650, "折返": 4119, "顾虑": 10602, "方而": 271, "郅": 2629, "招降": 1150, "传于": 867, "中郎将": 2026, "阻截": 667, "捕": 19031, "长史": 3177, "祸患": 864, "击杀": 15184, "回城": 4713, "中毒": 49352, "怀帝": 454, "主掌": 357, "次月": 4155, "疫病": 2304, "频生": 208, "令到": 2039, "被逼": 6068, "流民": 1373, "略阳": 1392, "爪牙": 1197, "失当": 1328, "李氏": 7104, "屡见": 316, "刘琨": 749, "撤去": 840, "檄文": 768, "倾覆": 1861, "排除异己": 259, "散骑常侍": 2476, "行台": 665, "出京": 762, "致令": 174, "遇害": 3903, "琅邪": 1651, "下邳": 1068, "钱大昕": 287, "廿二": 984, "考异": 304, "赵翼": 560, "试探": 9686, "荆棘": 7169, "高远": 3515, "惠灵顿": 1324, "北岛": 2719, "末端": 12481, "塔斯曼": 704, "喀里多尼亚": 1540, "斐济": 3566, "与众不同": 12699, "隆升": 263, "东印度公司": 2729, "亚伯": 2786, "西兰": 1363, "班克斯": 1402, "捕鲸": 2201, "扩增": 2600, "毛利": 8592, "文意": 783, "芥蒂": 832, "海床": 889, "昆士兰": 4259, "塔斯马尼亚": 1633, "卡托": 3002, "炮兵": 8718, "炮弹": 10247, "阻击": 4264, "克拉克": 8318, "伊丽莎白": 11761, "亚太地区": 3299, "联立": 1771, "威灵顿": 1715, "温润": 3696, "放牧": 3342, "南极罗斯": 18, "吉斯伯恩": 51, "尼尔逊": 192, "马尔堡": 651, "坎特伯雷": 1212, "区市": 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"黑帮": 10009, "脸书": 4188, "自承": 278, "打回去": 1306, "那人": 14006, "头破血流": 1537, "管中": 2076, "独董": 100, "遴选会": 22, "回文": 1253, "副董事长": 2336, "独厚": 127, "臆测": 1651, "争议案": 59, "适法": 53, "善尽": 212, "偏颇": 4561, "诚信": 26607, "地检署": 1780, "政治力": 172, "戕害": 395, "大维": 960, "诉愿": 422, "救济": 8903, "总工会": 2508, "协进会": 1353, "黄丝带": 336, "清大": 2674, "教师会": 874, "法人化": 169, "民进党籍": 595, "张善政": 226, "前行政院长": 188, "建请": 109, "沦落到": 1557, "社会系": 288, "副教授": 9322, "迟来": 2380, "聘任案": 6, "借机": 6076, "北社": 427, "吴福滨": 4, "纷扰": 1140, "无限上纲": 78, "修课": 550, "分班": 3611, "图利": 1112, "列席": 1510, "期中考": 755, "莫内": 489, "电力学院": 544, "阿尔卑斯": 4034, "艾克斯": 5644, "贝利": 10236, "新堡": 737, "安普": 1942, "亚琛": 1370, "多特蒙德": 3263, "不莱梅": 2744, "波鸿": 1217, "卡尔斯鲁厄": 1482, "海德堡大学": 548, "波昂": 192, "维也纳大学": 415, "鲁汶": 650, "根特": 833, "鹿特丹": 1852, "莱顿": 2864, "雪平": 844, "伯德": 2900, "乌普萨拉": 2143, "东京大学": 4730, "京都大学": 1897, "名古屋大学": 551, "早稻田大学": 1924, "关西": 8052, "庆应": 2324, "冈山": 6075, "千叶": 9264, "同志社": 496, "茶水": 6692, "立命馆": 417, "创价": 385, "筑波": 1743, "梨花": 13055, "高丽大学": 441, "浦项": 923, "成均馆大学": 248, "延世大学": 426, "匹兹堡": 3305, "蒙特娄": 749, "多伦多大学": 1985, "西澳": 1164, "史德": 233, "波希": 625, "市民们": 602, "宛如": 6328, "游憩": 1028, "散步": 21596, "大时代": 3077, "铁栏杆": 722, "时亦": 5737, "志工": 1791, "导览": 1743, "杨祥发": 18, "糖蛋白": 1282, "经济系": 2480, "宏图": 2372, "电机系": 1112, "联发": 881, "德音": 315, "航电": 1483, "廖敏雄": 43, "戴胜": 436, "尹衍梁": 94, "商学": 2097, "詹宏志": 100, "张凯": 2138, "周芷": 2617, "擦身而过": 911, "朱天心": 143, "余光中": 1126, "蓝星": 1727, "林文月": 220, "曾宝仪": 520, "蔓生": 628, "国企系": 22, "猪头皮": 615, "黄舒骏": 232, "杨昕曦": 45, "周华健": 5197, "苏有朋": 4300, "吴淡如": 210, "高怡平": 54, "齐豫": 1425, "徐世珍": 101, "社工组": 8, "怪兽": 32461, "环境卫生": 3508, "呱": 1827, "陈妍": 2144, "刘子": 3355, "明达": 890, "麦浪": 1327, "夏铸九": 31, "重访": 409, "去过": 42011, "饱餐一顿": 329, "强加于": 770, "交朋友": 10002, "飞散": 499, "南沙": 8809, "吴稚晖": 296, "苏曼殊": 213, "中国科学社": 201, "高师": 1223, "委员制": 57, "直皖": 225, "东南大学": 3826, "秉文": 374, "人间词话": 725, "常熟": 5560, "张家港": 3408, "锦丰": 1302, "铺筑": 323, "江阴": 7595, "国联": 3981, "边城": 3655, "黄文": 2441, "弼": 5622, "北五省": 192, "台长": 2625, "小洋": 813, "江防": 445, "合兴": 1142, "建桥": 1101, "筑基": 323, "侦": 7883, "日寇": 1919, "八一四": 145, "记道": 59, "空军节": 54, "一万余": 1380, "副处长": 3967, "毓庆": 139, "工作队": 1345, "敌机": 2728, "详密": 48, "情报网": 324, "美法": 447, "日机": 899, "苔": 17425, "一百四十几": 13, "中美英": 168, "围困战": 136, "十余万": 1120, "古宁头": 321, "搭机": 432, "驶入": 25022, "协防": 1239, "会道门": 264, "进藏": 1538, "精兵简政": 142, "增产节约": 60, "刘青山": 209, "张子善": 149, "分洪": 423, "自治机关": 267, "生产合作社": 290, "农业合作化": 153, "九三": 2524, "炮战": 653, "赫鲁晓夫": 2600, "案中": 4947, "定陵": 753, "陈镜开": 158, "最轻量级": 92, "挺举": 817, "两段": 8032, "浮水": 744, "白龙尾": 112, "部长会议": 1489, "四十周年": 490, "空对空": 1389, "空喀": 45, "试制": 2720, "探空火箭": 220, "南汇县": 282, "勘界": 395, "工联": 514, "彭真": 1087, "中共代表团": 197, "布加勒斯特": 2032, "缅甸联邦": 449, "指示信": 79, "九中全会": 37, "新丰江": 179, "刘志丹": 529, "反党": 1014, "美制": 1577, "十中全会": 74, "廖承志": 635, "高埼达之助": 31, "互派": 427, "地质科学院": 165, "沉于": 451, "太平湖": 481, "李富春": 387, "陈伯达": 604, "姚文元": 413, "代办": 17010, "柯利达": 152, "英方": 1670, "尿毒症": 4303, "北京植物园": 296, "宣传队": 552, "乡下": 12096, "核工业": 1722, "零四": 1832, "珍宝岛": 957, "躬": 3482, "柯西金": 156, "胡志明": 1684, "叶海亚": 308, "口信": 592, "北京军区": 1676, "郑维山": 84, "李雪峰": 143, "乒乓外交": 200, "转暖": 908, "工农兵学员": 162, "基辛格": 1474, "叶群": 312, "坠机": 2003, "马王堆汉墓": 361, "田中角荣": 417, "大平正芳": 182, "陈景润": 925, "打井": 1614, "碎片": 43512, "代总理": 212, "农垦局": 188, "六十一": 782, "团场": 351, "共产主义者": 2033, "铁托": 789, "西单": 3515, "魏京生": 147, "反革命罪": 219, "判刑": 158566, "务虚": 681, "期满": 14083, "林毅夫": 901, "致信": 1493, "八一七": 322, "撒切尔夫人": 793, "刑事犯罪": 3060, "政社": 127, "乡政府": 7938, "村民委员会": 13173, "粟裕": 2191, "鱼雷艇": 1466, "越南人民军": 509, "赤瓜礁": 235, "中子弹": 487, "实弹": 1411, "胡启立": 179, "美智子": 1021, "粮票": 1714, "银河号": 420, "辜振甫": 289, "政策性": 4222, "分税制": 505, "中宣部": 1672, "尼玛": 18747, "转世灵童": 675, "江八点": 25, "上访": 11958, "主办权": 1238, "收容": 3850, "双人": 11904, "物权法": 3145, "雪灾": 867, "力拓": 926, "上海办事处": 284, "美美": 16669, "追尾": 27551, "实名制": 6322, "王立军": 698, "芦山": 783, "金水桥": 810, "袭击案": 291, "月面": 4406, "软着陆": 669, "云南昆明": 1390, "青奥会": 657, "沉船": 3444, "统战部长": 178, "郭伯雄": 355, "褫夺": 894, "绑定": 68253, "工具箱": 7574, "快照": 3665, "润饰": 366, "伯克利分校": 1563, "加号": 3380, "归结于": 1766, "画笔": 5423, "菜单": 96792, "对话框": 35098, "调色板": 936, "选择器": 1744, "滴管": 1024, "画布": 2851, "自定义": 30212, "魔棒": 957, "适配": 6322, "贝兹": 1124, "铅笔": 16522, "喷": 99052, "橡皮": 10405, "墨水": 12022, "像素": 63710, "涂抹": 27541, "对齐": 8574, "切变": 347, "灰度": 2650, "滤镜": 10727, "添加到": 9750, "程序控制": 773, "遮罩": 803, "深及": 302, "会发布": 1049, "单号": 10333, "集思广益": 1333, "原生地": 508, "复刻": 3723, "正片": 3630, "润色": 2152, "高保真": 1344, "便携": 8741, "文件名": 10708, "合集": 18703, "和平面": 819, "子计划": 123, "正朔": 574, "第七章": 2070, "梁建国": 22, "苦心经营": 663, "隐隐": 6104, "病亡": 432, "点检": 867, "再也没有": 11757, "贸易业": 161, "全兴": 450, "独到之处": 1224, "根除": 4344, "慓": 41, "惨": 37659, "所至": 1963, "燔": 1092, "荆榛": 324, "啖": 1716, "军士": 3112, "四出": 640, "远较": 593, "有义": 1522, "岐王": 337, "威武": 8476, "承裕": 104, "崱": 133, "瓜州": 813, "镠": 159, "周延": 565, "诰": 3731, "避祸": 740, "无罪": 13043, "掖": 3225, "留于": 742, "三十名": 413, "愤愤": 513, "此辈": 208, "投于": 250, "浊流": 467, "第二章": 7999, "士卒": 1873, "陪侍": 557, "克宁": 2173, "承业": 237, "怀疑成": 47, "倒向": 1830, "河北柏乡": 23, "残暴": 5611, "周德威": 107, "养子": 5317, "马步": 1719, "父皇": 914, "不得人心": 650, "雄军": 262, "濮州": 263, "文礼": 443, "杀成": 234, "招讨使": 484, "王彦章": 201, "郓州": 433, "围堵": 4360, "赵岩": 364, "继岌": 179, "郭崇韬": 235, "内忧": 215, "招回": 252, "不务": 259, "自取": 2825, "伶人": 568, "伶": 30731, "暂扣": 13409, "密报": 611, "涣散": 1658, "御前": 2233, "出库": 5122, "妆奁": 95, "惟此": 408, "鬻": 829, "惶恐": 4423, "赵在礼": 86, "赍": 679, "诟": 575, "饥": 10672, "输钱": 2953, "绍荣": 174, "中流": 2002, "罂": 795, "子谷": 142, "隘": 4093, "好言": 332, "陛下": 9881, "太晚": 8695, "叱": 2304, "惜物": 57, "归罪于": 666, "不测": 1910, "吾身": 1128, "投水": 451, "杀尽": 630, "诛除": 152, "冗余": 4648, "明君": 1633, "女婿": 15387, "瀛州": 548, "猜忌": 2780, "怀有": 5448, "时后": 1562, "急欲": 337, "用后": 4529, "沈湎": 33, "壅": 1560, "自应": 541, "少主": 962, "出奔": 826, "大去": 221, "扶危": 285, "颠": 9376, "安然": 5057, "杨光远": 114, "辽太宗": 343, "急令": 201, "苦苦": 8336, "哀求": 2544, "解围": 2962, "依约": 1966, "渥": 3839, "颢": 1583, "约分": 1369, "于成": 493, "以次": 891, "善事": 2576, "激切": 121, "气色": 4144, "讽": 2967, "备位": 82, "韩熙载": 302, "欺负": 38784, "瑾": 10075, "既而": 782, "郁郁而终": 307, "俭朴": 1685, "颇得": 450, "用事": 1168, "羁旅": 1059, "察视": 32, "婚丧": 360, "盛暑": 143, "何用": 4341, "吴人": 620, "烈祖": 379, "见机": 348, "国一": 994, "约同": 241, "安民": 1943, "死对头": 1465, "羲之": 316, "李仁": 1947, "群小": 409, "国主": 816, "周军": 503, "深紫": 566, "淡紫": 499, "汉荆": 9, "贡品": 1935, "唐唐": 978, "向后": 14118, "索要": 54140, "不济": 4035, "乱军": 637, "潭州": 1192, "颇盛": 73, "纵酒": 443, "荒淫": 1024, "马光": 366, "武平": 1252, "威也": 253, "衡山": 3042, "贪得无厌": 993, "劣政": 32, "贤士": 771, "以岭": 160, "唐世": 225, "周杰": 2321, "赵光": 744, "巡官": 242, "避乱": 398, "辟置": 6, "不宁": 1368, "邕州": 426, "立威": 583, "宫女": 4460, "新立": 2481, "贪求": 377, "无厌": 576, "恭谨": 260, "守贞": 808, "廷光": 50, "自恃": 1007, "年成": 1113, "指斥": 340, "其头": 855, "送与": 627, "代北": 212, "问罪": 774, "延广": 80, "多难": 5891, "屈尊": 418, "屈辱": 3789, "敌意": 4663, "出气": 3764, "志先": 75, "荣于": 257, "取笑": 3099, "蝗虫": 3075, "白沟河": 84, "定兴": 404, "白沟": 1360, "贿": 11032, "公行": 828, "张彦": 760, "打草": 316, "北返": 259, "辅国": 2089, "大杀": 1142, "恰巧": 6201, "河南濮阳": 352, "减除": 1569, "励行": 155, "流回": 603, "汉帝": 536, "杨衮": 68, "稳重": 10602, "临阵": 977, "延聘": 188, "二千六百": 171, "九十四": 1046, "三百三十六": 87, "四万二千": 85, "四百四十四": 47, "一万八千": 493, "七百五十六": 25, "班师": 764, "二州": 1512, "陈桥驿": 148, "帝后": 847, "越南汉": 79, "先易后难": 388, "新主": 270, "假道": 339, "凤州": 300, "六十多": 1518, "宠妃": 6417, "花蕊": 2116, "城上": 1945, "深宫": 6129, "十四万": 421, "解甲": 319, "更无": 2378, "女侍": 667, "中卢": 254, "阉割": 4957, "进用": 254, "宋廷": 355, "死光": 1808, "词坛": 309, "滥杀": 515, "酣": 4104, "愁思": 817, "悲歌": 3141, "夹攻": 491, "钱弘佐": 78, "恭顺": 525, "陈洪": 844, "援兵": 1053, "互通有无": 427, "独立成": 984, "忠于职守": 506, "诏谕": 169, "徇私": 619, "求才": 147, "众所知": 36, "积弊": 398, "新规": 5858, "逐条": 1855, "擢": 3442, "诛戮": 153, "怠惰": 631, "任满": 589, "位高权重": 623, "冗滥": 15, "重美": 66, "监修": 2167, "专横": 942, "为所欲为": 2359, "防御使": 712, "藩主": 3926, "威州": 206, "府南": 430, "第五章": 4914, "台省": 237, "拢络": 122, "尚书左右丞": 20, "正四品": 382, "金銮殿": 386, "正二品": 602, "侍从": 3088, "参知政事": 854, "三司使": 181, "专管": 693, "相承": 589, "缘自": 252, "臣僚": 229, "宰臣": 120, "溥": 4219, "枢密": 1047, "左右神策军": 16, "中尉": 5666, "侵夺": 274, "宣徽使": 142, "繁杂": 4294, "汇编为": 44, "刑统": 79, "略加": 919, "增删": 1352, "燕云": 464, "卓歇图": 8, "阏": 866, "出猎": 416, "饮宴": 433, "黠": 828, "嘎斯": 237, "收留": 3992, "默记": 257, "耶律休哥": 119, "散处": 62, "黄头": 545, "对调": 1871, "灵州": 606, "党项羌": 62, "世物": 44, "晋封": 850, "守护者": 14305, "牂柯": 212, "罗城": 3252, "清平": 2190, "向高": 3006, "黑爨": 5, "军师": 8286, "春游": 2375, "子都": 1222, "扩编": 1332, "龙虎": 5997, "神武": 4685, "收编": 1542, "银枪": 347, "此职": 740, "严明": 2159, "张昭": 1042, "脸上": 82762, "刺字": 354, "军号": 617, "关津": 191, "识认": 41, "人畜": 3157, "毙": 4025, "征发": 446, "攻城": 8015, "土山": 905, "十五岁": 3181, "自备": 4104, "共得": 1707, "老弱": 650, "调发": 120, "丁壮": 154, "乡兵": 153, "赋役": 483, "棋图": 51, "六七十年": 134, "草芥": 643, "惨杀": 112, "苛重": 37, "流徙": 157, "时溥": 108, "濠": 5873, "六万多": 328, "精华区": 161, "在籍": 981, "焚掠": 84, "一空": 2742, "数百里": 378, "相州": 922, "二十八万": 133, "三万余": 631, "一百四十": 348, "一百二十万": 212, "想见": 7625, "两百七十余": 4, "五十五万": 42, "五十三万": 31, "二百三十万": 36, "第八章": 1324, "交迭": 102, "熄": 3773, "经济力": 136, "耕织": 352, "杭州城": 648, "大起": 986, "吃光": 2229, "黄河水": 843, "水浸": 2849, "远胜于": 939, "灭蝗": 36, "佃户": 672, "二百九十一": 77, "其民": 605, "既得": 541, "葺": 1098, "数倍": 2625, "誉称": 193, "无事": 8740, "权德舆": 117, "句容": 1889, "少者": 515, "多者": 1572, "万顷": 2159, "江水": 7133, "扰攘": 345, "鉴湖": 882, "钱氏": 566, "撩": 17819, "专一": 6722, "募": 2781, "免征": 5531, "四万余": 179, "灌田": 132, "关闸": 1106, "稼穑": 429, "仓廪": 624, "余粮": 744, "纂要": 324, "养蚕": 1245, "壶嘴": 478, "制茶": 680, "晒": 54151, "书肆": 65, "印刷品": 2146, "字书": 501, "蜀中": 1032, "中止": 12809, "刻印": 2978, "陶艺": 1715, "民窑": 479, "分道扬镳": 2341, "争奇斗艳": 804, "色釉": 179, "御窑": 241, "定窑": 355, "雨过天青": 92, "创举": 1324, "造瓷": 15, "窑厂": 1271, "澄心堂": 82, "擅写": 88, "作诗": 4874, "纸品": 869, "佼佼者": 4648, "茶商": 471, "买回": 4751, "马前": 859, "福州城": 145, "泉州城": 52, "猛火": 981, "珍玩": 181, "免为": 213, "夜宴": 2221, "行乐": 2436, "第九章": 1059, "兴发": 1166, "弑君": 753, "文明史": 1699, "侍奉": 2973, "将相": 2222, "四十多年": 622, "提携": 2572, "贤愚": 501, "灾祸": 3001, "避世": 1059, "隐遁": 346, "名利": 5980, "二百一十六": 102, "隐逸": 878, "峭": 2913, "民主性": 155, "虚化": 5162, "形化": 71, "虚虚": 176, "相命": 145, "民本": 659, "李后主": 514, "虞美人": 1499, "无穷无尽": 2434, "饱经沧桑": 330, "沉痛": 1807, "文人学士": 250, "司空图": 252, "韦庄": 826, "庸俗": 3745, "花间": 3200, "深沉": 8818, "回味无穷": 2569, "采桑子": 395, "谒": 4757, "遗有": 392, "虚浮": 466, "集刻": 13, "主词": 634, "玉楼春": 377, "慨叹": 1210, "怀恋": 753, "伤感": 11717, "深挚": 335, "浪淘沙": 2379, "专写": 135, "窠臼": 522, "唐书": 762, "三川": 1643, "搜": 97067, "碑碣": 329, "照抄": 1530, "详略": 883, "零落": 2578, "第十一章": 531, "仁裕": 187, "遗事": 871, "摭": 520, "孙光宪": 109, "琐言": 142, "云岩": 725, "虎丘": 5659, "崇道": 253, "生根": 5469, "所受": 51514, "宗惠": 53, "顿悟": 2972, "怀让": 168, "南岳": 3419, "三句": 3973, "函盖": 89, "截断": 3728, "随波": 763, "逐浪": 655, "宗法": 1510, "杨歧": 47, "流于形式": 979, "呵": 11200, "亡失": 93, "观应": 155, "浮滥": 99, "整饬": 397, "刘海": 19786, "势态": 309, "紫霞": 4337, "以道": 778, "入道": 1458, "广圣": 53, "经住": 81, "安神": 8811, "无别": 666, "立志": 8964, "养气": 808, "谭子化书": 6, "分章": 221, "真义": 434, "父辈": 4151, "石恪": 28, "优裕": 299, "集中性": 271, "仝": 5667, "陶情": 102, "崇山峻岭": 796, "匡庐": 464, "平添": 1290, "萧索": 892, "苍凉": 1652, "行旅": 655, "秋山": 3637, "巨然": 104, "擅用": 450, "皴": 907, "淡墨": 548, "平淡": 16439, "寒林": 407, "卵石": 1767, "萧翼": 85, "赚": 85422, "丛树": 104, "珍禽": 567, "黄家": 4910, "写生": 3408, "珍禽图": 21, "竹图": 80, "顾闳中": 69, "珍品": 6487, "丑怪": 108, "奇诡": 302, "丈人": 1678, "遒健": 17, "神彩": 172, "心惊": 1082, "悸": 3111, "杨凝式": 81, "蒴": 751, "取态": 172, "离乱": 318, "回光": 379, "夏热": 721, "书坛": 184, "火焰喷射器": 531, "制瓷": 349, "火器": 4234, "历书": 682, "分母": 7388, "施药": 821, "神效": 360, "广英": 65, "韩鄂": 12, "薯": 10233, "蓣": 963, "养蜂": 2041, "酸枣": 2794, "帚": 1418, "护岸": 473, "堵口": 159, "护堤": 329, "薪柴": 192, "土石": 1305, "捆扎": 1043, "捆": 10697, "济运": 35, "焦灼": 1153, "路振": 154, "长编": 754, "十五万": 1865, "帆樯": 143, "航高": 32, "重上": 759, "急攻": 173, "火油": 209, "俄而": 313, "其众": 343, "惶": 1056, "棹": 4142, "蹙": 1756, "册中": 416, "下册": 6547, "五德终始说": 68, "自诩为": 870, "特称": 422, "杯酒": 8929, "才力": 406, "重文": 398, "影响所及": 140, "相随": 4605, "摆脱不了": 1105, "掳去": 250, "杭州临安": 185, "张世杰": 377, "全军覆灭": 353, "千载": 4394, "邓广铭": 181, "漆侠": 42, "空前绝后": 1063, "尊师重道": 441, "忤": 888, "加身": 898, "出城": 3563, "夜里": 26441, "酣睡": 743, "徐俊": 628, "委以": 417, "集中到": 2782, "下风": 2346, "雪夜": 1274, "以先": 1804, "掉头": 12871, "布匹": 1654, "赎回": 20978, "暂告": 183, "挥师": 573, "大百科": 2012, "众臣": 337, "驴车": 213, "美兵": 54, "西路军": 887, "副将": 3518, "杨业": 860, "中伏": 628, "好水": 1087, "厌战": 569, "所到之处": 1359, "生活品": 151, "大蜀": 72, "附庸风雅": 452, "善书": 367, "蹊跷": 1659, "烛影": 594, "抛出": 8621, "遗命": 559, "金匮": 790, "赵德昭": 129, "逼死": 2313, "德芳": 426, "赵廷": 362, "美到": 3070, "房州": 205, "僖": 1751, "王继恩": 106, "掠到": 53, "寇准": 897, "州城": 1096, "几番": 2122, "廿万": 46, "胜利者": 3054, "战败者": 153, "巨款": 2045, "城下之盟": 145, "失宠": 2073, "逢迎": 1334, "祥和": 6237, "王旦": 155, "祥瑞": 2704, "深得": 4746, "临幸": 539, "嫔妃": 2049, "廿日": 278, "军国": 832, "垂帘听政": 787, "屡战屡败": 750, "宋英": 167, "宗夏": 40, "宋哲宗": 651, "狄青": 784, "侬智高": 242, "结伙": 17046, "结党营私": 297, "半个月": 28981, "名分": 1406, "皇考": 425, "总体来说": 13743, "虎视眈眈": 1532, "任其": 2473, "青苗": 4135, "流民图": 63, "惨不忍睹": 4774, "惠卿": 219, "肆意妄为": 418, "愿为": 2414, "五陵": 977, "轻薄": 10915, "叹": 20192, "无庸": 324, "立意": 4841, "求新": 999, "消化": 83862, "十余项": 88, "欲速不达": 89, "扰民": 2678, "蔡京": 978, "品性": 2173, "评论道": 1158, "无所不至": 160, "所及": 2752, "相提并论": 5467, "一病不起": 299, "佣": 2602, "报复性": 1121, "闲置": 9964, "前边": 4469, "贬斥": 452, "绍述": 63, "聚敛": 643, "养民": 166, "稍有": 12000, "丰裕": 822, "横山": 6638, "轻佻": 999, "迷醉": 1077, "逛": 33149, "自封为": 401, "万岁山": 193, "慈溪": 3461, "飞禽走兽": 1058, "应有尽有": 3155, "应奉局": 16, "造作": 2061, "奇花": 723, "花石纲": 130, "民怨沸腾": 201, "疏于": 7015, "佞": 1186, "属意": 634, "聚财": 1341, "奸佞": 479, "奸党": 158, "拥趸": 1474, "外州": 186, "穷奢极欲": 232, "曲意": 384, "盘剥": 578, "黎民": 843, "苛捐杂税": 292, "占居": 198, "三十六名": 146, "十月初": 2841, "青溪": 1346, "浙江淳安": 117, "闻风": 720, "五十余": 778, "童贯": 588, "凯歌": 2215, "高奏": 179, "靖西": 2349, "近在咫尺": 1683, "败绩": 929, "残兵": 600, "痛击": 922, "溃退": 535, "还得": 44227, "破城": 608, "不可战胜": 814, "天朝": 14565, "掠去": 157, "借故": 3707, "克扣": 2756, "平滦": 22, "更生": 1653, "知州": 2822, "滞于": 200, "摧枯拉朽": 511, "大惊失色": 762, "啼哭": 1840, "战和": 1088, "举棋不定": 587, "万般无奈": 500, "休战": 1774, "掣肘": 900, "诸路": 492, "慌张": 3896, "六甲": 1687, "神兵": 9628, "把戏": 2677, "掳": 1024, "贵戚": 307, "国城": 730, "依兰县": 2149, "女眷": 342, "德公": 417, "客死异乡": 116, "大楚": 225, "行过": 866, "临安府": 374, "扑": 24883, "无路": 3720, "漂泊": 6014, "天候": 728, "兀术": 354, "韩世忠": 688, "退路": 3000, "火攻": 1278, "刘之": 497, "颖昌": 72, "岳家军": 659, "撼山": 155, "撼": 3142, "连下": 786, "岳王庙": 187, "淮水": 752, "廿五万": 12, "莫须有": 1489, "岳云": 937, "张宪": 551, "桧": 2591, "朝夕": 1977, "必杀": 6877, "接人": 1899, "丁卯": 3049, "接回": 2513, "主和": 4535, "北人": 1207, "内臣": 241, "孙儿": 1048, "廿五年": 115, "一命呜呼": 363, "璩": 772, "卅二年": 61, "卅一年": 83, "退意": 256, "五十六": 2590, "八日": 2728, "余党": 550, "李显忠": 122, "裁汰": 110, "让位于": 858, "疯癫": 1653, "临安城": 122, "新君": 1175, "操守": 2958, "煽风点火": 641, "门上": 5936, "笼络": 1365, "蛊惑人心": 332, "众矢之的": 863, "杨皇后": 129, "本有": 3102, "闰八月": 199, "韬晦": 168, "步步紧逼": 560, "唇亡齿寒": 354, "三峰": 1332, "后患": 1920, "孟珙": 156, "遗骨": 1468, "告慰": 487, "在天之灵": 365, "石梯": 527, "子才": 561, "七月五日": 73, "贻误": 1112, "此役": 2187, "大坏": 537, "甘心": 3401, "王坚": 396, "张钰": 666, "流矢": 304, "幼弟": 373, "皇储": 598, "先天不足": 1236, "廿六日": 261, "葛岭": 230, "妻妾": 2275, "斗蟋蟀": 173, "樊城": 4164, "七月九日": 118, "威逼": 3873, "赴任": 1566, "监押": 69, "人心惶惶": 1049, "二月初": 1887, "受降": 1452, "逃出": 10000, "小朝廷": 317, "陈宜中": 79, "十一岁": 1405, "紧追在后": 118, "总攻": 1265, "溃败": 2226, "腥": 9339, "飙风": 737, "瑁": 924, "岢岚": 523, "原平": 3044, "繁峙": 256, "阜平": 475, "容城": 447, "此线": 1593, "献出": 3689, "白干": 1518, "开扩": 120, "熙河": 167, "湟水": 239, "失地": 2040, "梅山": 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1708, "痛殴": 313, "传出去": 1183, "镇暴": 421, "挑拨离间": 769, "三轮车夫": 213, "干下": 196, "比喻成": 1388, "公敌": 1867, "马来西亚人": 646, "落泪": 6154, "忙碌": 13222, "深怕": 616, "蕞尔小国": 64, "大巴": 13566, "埃克森美孚": 444, "建屋": 749, "组屋": 423, "新镇": 5190, "屋里": 15742, "低至": 2589, "新航": 726, "勒南": 226, "年青": 4580, "赢者": 519, "吴作栋": 198, "李显龙": 837, "民主联盟": 1583, "刘程": 136, "杨荣文": 67, "中共十五大": 169, "副组长": 2819, "打篮球": 11572, "地质局": 555, "技术员": 4629, "政工干部": 262, "政治处": 1577, "孙大光": 39, "地矿部": 69, "党工": 224, "八九": 4246, "民运": 1078, "电传": 1133, "劝慰": 828, "防汛": 2361, "总指挥部": 1025, "抗灾": 562, "九位": 1188, "粮价": 613, "还不如": 35841, "值钱": 25662, "直补": 2789, "农资": 3899, "E座": 346, "电荒": 205, "高压线": 3188, "参股": 2984, "民主法制": 420, "放贷": 4947, "抬高": 8989, "规划成": 240, "滋长": 1122, "均价": 8534, "初任": 891, "疯涨": 1066, "平米": 32712, "回访": 3765, "国事访问": 1075, "冰之": 1724, "切磋": 3089, "互惠": 1847, "新闻公报": 505, "经贸往来": 286, "温总": 221, "抗震": 6345, "重灾": 115, "手臂": 41806, "轻伤": 444003, "良心": 30705, "李长江": 171, "乳业": 2758, "坦诚": 7393, "铭记": 4709, "下基层": 533, "体察": 939, "倾听": 13771, "第一手材料": 194, "死路一条": 1030, "牢记": 7360, "清醒": 24386, "明辨是非": 822, "葬送": 1373, "中国之声": 1205, "直播间": 4295, "艾滋病人": 965, "犯错误": 3982, "代政": 76, "韩联社": 758, "无核化": 932, "朝美会谈": 21, "六方": 2411, "平安南道": 534, "桧仓郡": 11, "毛岸英": 582, "扫墓": 3209, "最高层": 1161, "负有": 9208, "歉疚": 506, "轭": 6730, "老马": 4288, "最后一刻": 3055, "绝不": 23416, "松套": 73, "缺憾": 2645, "祸福": 1758, "倾注": 2621, "心血": 7654, "做完": 36006, "九死": 549, "九次": 2488, "要死": 18726, "榜上": 3706, "错失": 4387, "戏称": 2786, "作秀": 2186, "挞伐": 402, "推卸": 3413, "投机分子": 320, "怒斥": 2167, "梦得": 794, "云松": 416, "卫星通信": 853, "新天": 1438, "几十亿美元": 229, "摩根大通": 1616, "瑞银": 2155, "德意志银行": 936, "小萌": 2966, "如春": 1003, "雷曼": 3090, "波士顿银行": 20, "刘春": 1134, "银监会": 4750, "督学": 884, "士其": 85, "秀兰": 1705, "殷实": 1494, "温氏": 545, "宜兴": 4311, "代人": 3225, "名门望族": 526, "武状元": 930, "认祖归宗": 232, "育女": 275, "亲任": 278, "炜": 7765, "落马": 2394, "明镜": 3927, "什锦": 2146, "第三卷": 1315, "赵正": 831, "诔": 716, "古训": 738, "祖龙": 1641, "平舒道": 15, "滈池君": 9, "苏林": 272, "始皇": 1141, "十三岁": 2243, "嫪毐": 404, "三十九岁": 274, "五十岁": 2624, "出巡": 1359, "两千余": 593, "急躁": 5921, "大君": 1477, "煌煌": 1060, "天庭": 3272, "九歌": 3088, "吉日": 8084, "愉": 3582, "秦昭王": 562, "异人": 1959, "选定为": 603, "孝文王": 146, "夏姬": 401, "远大": 13173, "奇货可居": 231, "年事已高": 1205, "如无意外": 329, "烟消云散": 1697, "大惊": 1779, "问计": 301, "巧施": 187, "才德兼备": 63, "立异": 181, "感念": 1268, "恩德": 3007, "国舅": 393, "一帆风顺": 4418, "孝成": 472, "厚礼": 477, "相送": 4594, "一无所得": 238, "大悦": 2019, "讶": 1355, "比得上": 2217, "文信侯": 57, "坐上": 9235, "赠给": 1379, "最力": 245, "不韦": 166, "产妇": 15893, "作大": 1045, "足月": 1907, "绝无": 3462, "曲笔": 145, "污蔑": 2986, "可信性": 656, "记下": 7778, "怀胎": 2527, "妊娠": 29815, "说起": 29717, "实属": 4340, "三川郡": 101, "采邑": 651, "惟独": 1278, "雷同": 5074, "前文": 2029, "绝好": 1302, "自相矛盾": 2399, "仲父": 200, "收留下来": 23, "名扬天下": 617, "孟尝君": 1187, "之强": 1039, "招揽": 7066, "礼聘": 266, "安邑": 619, "夏县": 1186, "屯留": 1689, "皆因": 1565, "连坐": 1356, "岷县": 2174, "饶阳县": 1628, "探微": 183, "箭镞": 198, "亡故": 987, "藕断丝连": 1720, "唯恐": 2120, "降临": 13697, "暗地": 1178, "阳具": 705, "桐木": 1669, "耳中": 993, "大感兴趣": 72, "找人": 28491, "假意": 3301, "腐刑": 192, "胡须": 6051, "服侍": 2433, "痴狂": 1624, "避人耳目": 67, "凤翔县": 1741, "山阳": 9715, "僮": 1844, "封侯": 2023, "奸": 11426, "杀敌": 3506, "淫乱": 2283, "彻查": 1010, "孤注一掷": 1230, "仆": 13244, "受骗": 10346, "文君": 1827, "秦相": 266, "句读": 546, "乌合之众": 1416, "不堪一击": 1779, "悬红": 174, "车裂": 395, "亲母": 177, "余怒": 98, "未消": 719, "蒺藜": 1295, "责打": 227, "进谏": 865, "齐国人": 369, "有损": 4901, "天下人": 2490, "信服": 3980, "进献": 722, "忠言": 668, "茅塞顿开": 631, "厚葬": 453, "甘泉宫": 152, "一番话": 1428, "朝堂": 869, "牵连到": 436, "打消": 3878, "失职": 4046, "名声显赫": 182, "放过": 16709, "服毒": 1145, "饮鸩": 100, "晋人": 562, "疑案": 921, "谜案": 683, "流放至": 367, "若未": 1207, "怒气": 5573, "胆敢": 1080, "为奴": 1584, "吉川": 2734, "出乎": 3228, "扫清": 1296, "名实相符": 82, "郑国渠": 259, "沟洫": 109, "三十六岁": 444, "建议者": 117, "细作": 238, "泾水": 248, "瓠": 1469, "水渠": 2917, "洛水": 1193, "蝗灾": 415, "混浊": 4506, "盐碱地": 823, "逐客": 429, "利大于弊": 1448, "逐客令": 117, "追回": 157081, "口吃": 7267, "毛病": 48034, "才学": 2278, "性恶": 618, "蠹": 2954, "孤愤": 324, "难言": 1423, "寡人": 1631, "要胁": 634, "珍宝": 6068, "疑心": 2171, "结仇": 425, "诬陷": 3577, "反悔": 4604, "夜长梦多": 256, "心迹": 850, "明察秋毫": 457, "明察": 388, "暗处": 1726, "奸邪": 428, "教本": 271, "勇力": 345, "毫不客气": 1017, "议者": 160, "侠客": 9330, "御者": 194, "徵调": 252, "蛀虫": 1037, "总方针": 162, "不动声色": 1891, "付上": 340, "智伯": 467, "敌手": 2247, "吝惜": 968, "送礼": 14559, "打乱": 5736, "计谋": 3457, "良将": 838, "帝业": 387, "稳住": 3085, "逐个": 4976, "唇齿相依": 231, "赵燕": 594, "阏与": 95, "左权县": 1810, "六座": 1068, "河北省保定": 2016, "磁县": 3429, "漳水": 310, "九座": 456, "武城": 3788, "扈辄": 35, "罢手": 637, "急命": 49, "亲兵": 519, "冲出": 10039, "奔回": 167, "国援": 93, "王安": 1486, "挽回": 27481, "内史": 1753, "兵卒": 563, "灭国": 1063, "作好": 3921, "擒获": 1444, "窘境": 2601, "合击": 1719, "出走": 5886, "迁进": 187, "倍加": 1566, "赵公": 643, "子嘉": 196, "几百": 25696, "残部": 1193, "王向东": 28, "沦亡": 304, "安于现状": 1292, "蛮荒": 3544, "殊死": 1124, "田余庆": 146, "余地": 8431, "民愤": 619, "鸿沟": 2963, "皆备": 270, "易守难攻": 537, "死守": 2746, "强攻": 2647, "奏效": 3529, "渠水": 158, "灌入": 1893, "虎符": 850, "两半": 6055, "秋风扫落叶": 219, "云梦秦简": 28, "钩沉": 218, "推托": 1101, "对攻": 962, "微辞": 107, "附和": 2706, "楚顷襄王": 149, "牵动": 2870, "楚人": 1604, "温床": 1636, "吴广": 1439, "贬至": 103, "汝南县": 6672, "寝": 9370, "临泉县": 8277, "鄢": 10087, "郡城": 658, "宝丰县": 2019, "约会": 34529, "韩人": 231, "连着": 9006, "三天三夜": 1787, "负刍": 110, "一击": 14696, "一败涂地": 1258, "深入至": 54, "更感": 227, "登门造访": 77, "还乡": 2434, "但见": 2965, "面色": 8230, "不豫": 138, "送行": 2603, "挣下": 117, "家业": 2399, "直扑": 428, "久攻": 532, "木牍": 150, "辛巳": 2296, "镇慑": 33, "余孽": 621, "蕲县": 102, "要好": 38546, "丹心": 3567, "逃归": 182, "力不能及": 164, "武指": 225, "劝诫": 1099, "欺侮": 961, "触动": 10185, "伏在": 1261, "秦舞阳": 213, "杨宽": 373, "送死": 1473, "短视": 1418, "大祸": 1320, "下向": 2810, "田光": 1563, "卫国人": 171, "自戕": 225, "临头": 678, "千斤": 1834, "脑袋": 28649, "带同": 985, "弩机": 189, "北关": 5616, "说尽": 808, "好话": 3277, "咸阳宫": 158, "捧着": 4337, "樊于期": 83, "匣子": 1686, "台阶": 15933, "脸色": 14278, "威严": 3936, "递上": 1100, "淬": 2457, "焠": 54, "人血": 2521, "濡": 3214, "露出来": 3593, "袖子": 6145, "刺向": 2070, "挣断": 151, "拔剑": 1542, "剑身": 800, "追赶": 29544, "太过": 30428, "吓": 32709, "发呆": 7285, "侍医": 66, "药囊": 147, "扔去": 732, "挡": 79858, "拔出": 8050, "砍断": 2550, "左腿": 7321, "倒地": 41026, "扔向": 2362, "撞在": 4536, "砍": 67510, "镒": 1189, "大发雷霆": 1225, "王嘉": 4250, "人头": 10789, "忍让": 3344, "削为": 40, "种下": 3571, "酒宴": 1056, "顿感": 660, "慌忙": 1916, "将门": 4049, "措手不及": 2509, "埋怨": 6977, "言听计从": 891, "赵卫": 121, "迁居到": 384, "后卫": 28966, "其解": 613, "剪除": 1441, "岳南": 289, "赂": 1038, "吴昊": 625, "内因": 7866, "弩弓": 239, "实名": 22028, "从头到尾": 7095, "行踪": 4412, "势均力敌": 1790, "坐待": 265, "田宅": 555, "专事": 483, "轮番": 3327, "勇悍": 123, "兵戎": 116, "列国": 2440, "遣回": 143, "杜正胜": 183, "朝不保夕": 328, "各怀鬼胎": 312, "廉颇": 3909, "趁火打劫": 657, "迎战": 7172, "铠甲": 40287, "临潼区": 3504, "更形": 190, "合理化": 2264, "眇": 1384, "诛暴": 55, "王绾": 54, "制书": 114, "钮": 6490, "自谦": 880, "高处不胜寒": 782, "同号": 917, "黔首": 231, "磨成": 4203, "世传": 597, "传国玺": 236, "玉工": 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"宁阳": 825, "平阴县": 1829, "长清县": 121, "鲁能": 6176, "下市": 482, "历城区": 3725, "商河": 2207, "章丘县": 110, "主赛场": 85, "摩托艇": 812, "黑牛": 1802, "干旱少雨": 232, "存水": 1153, "热空气": 1003, "卧虎": 972, "徒骇河": 100, "泉群": 190, "喷涌": 1103, "茂山": 645, "鹊山": 145, "塘坝": 544, "土坝": 516, "光顾": 5903, "槐荫区": 2641, "天桥区": 3042, "全福": 862, "洋商": 148, "一巷": 4901, "路三": 1064, "四场": 3053, "估衣": 60, "筐": 8668, "万紫巷": 6, "货栈": 306, "酱园": 325, "经一路": 524, "七路": 2620, "盖成": 262, "新老": 4264, "楼体": 794, "魏家": 1429, "高档": 24192, "恒隆": 1619, "档次": 27881, "世茂": 1891, "日领": 224, "济南商场": 16, "银座": 6648, "奥特莱斯": 1375, "丁豪": 44, "中海": 4632, "环宇": 1742, "华联商厦": 432, "嘉华": 2804, "五洲": 4202, "维华": 155, "华润万家": 1700, "华联": 5062, "家悦": 884, "超商": 955, "机床厂": 547, "重工": 7393, "石化集团": 272, "锅炉": 18215, "车辆厂": 1449, "浪潮集团": 106, "宏济": 189, "德馨": 825, "轻骑集团": 17, "针织": 6815, "小鸭": 4762, "金钟": 7925, "衡器": 451, "积成": 287, "平阴": 623, "饴": 1085, "油酥": 1920, "微机": 5294, "云计算": 8814, "迪斯尼": 4525, "华谊": 3973, "电影城": 1070, "济南火车站": 346, "始发站": 1743, "直飞": 2408, "台办": 630, "首航": 1469, "济南机场": 118, "吞吐": 1464, "铁路桥": 3077, "立交桥": 49895, "辛庄": 4338, "经七路": 312, "共青团": 5499, "解放路": 10737, "村街": 1931, "湖东路": 2004, "花园路": 3473, "花园东路": 134, "无影山": 243, "东口": 5089, "纵是": 333, "山路": 39911, "路向": 3356, "二环北路": 1297, "奥体": 1591, "老虎洞": 185, "浆水": 701, "大岭": 1443, "转山": 798, "八路": 5819, "开建": 1434, "自动车": 2477, "营业所": 2783, "投币": 2508, "空调车": 1004, "八折": 1550, "起步费": 88, "摆渡": 6613, "月票": 2065, "专用车": 1443, "并排": 4281, "发车站": 124, "镇政府": 21050, "字头": 5188, "长运": 433, "规划局": 3824, "土建工程": 1694, "柳庄": 407, "长途汽车站": 3585, "附加费": 1764, "上车": 54685, "七折": 893, "婉约派": 417, "墨客": 486, "骚人": 1273, "选进": 801, "胜迹": 456, "玉函山": 47, "大明湖畔": 442, "后殿": 1328, "池子": 3289, "流杯池": 84, "濯": 3703, "缨": 5563, "赶庙会": 132, "上香": 4092, "设点": 1004, "三十日": 4527, "地藏王菩萨": 962, "普度": 1350, "苦海": 2267, "超度": 2720, "亡魂": 1381, "纸船": 587, "觞": 6740, "围坐": 1531, "溪水": 4064, "酒杯": 7506, "托盘": 6679, "被罚": 5147, "竞发": 154, "城东": 15500, "扫码": 5817, "抽奖": 14583, "济南人": 537, "一三": 3008, "对换": 1205, "比较句": 56, "问句": 1857, "补语": 1731, "俚语": 2290, "征订": 714, "零售价": 3030, "生活日报": 44, "腾空": 2604, "红叶": 9691, "补给区": 60, "湖内": 687, "遍植": 278, "产有": 165, "旧日": 2053, "街中": 930, "百花洲": 470, "九点一": 42, "晨光": 5169, "九点": 9625, "砌": 11440, "九如": 771, "西营镇": 163, "赛马场": 919, "万德": 883, "埠村": 3043, "澄波": 242, "环秀": 331, "草包": 994, "把子": 2927, "英才": 4019, "回民中学": 121, "德润": 615, "九年一贯制": 280, "俊德": 352, "锦屏": 3307, "汇文": 626, "竞业": 2066, "崇德中学": 45, "育英": 1290, "育贤": 468, "第三十七": 1041, "第五十二": 1554, "第五十六": 1195, "三中": 5275, "刁": 15806, "垛": 5025, "枣园": 1765, "官庄": 1626, "洪范": 704, "怀仁": 2598, "玉皇庙": 664, "附属小学": 979, "汇泉": 626, "景山": 2102, "智远": 264, "燕翔": 163, "科苑": 1162, "义和": 1499, "棋盘街": 83, "盛景": 943, "育新": 737, "营市": 100, "东街": 14994, "中医院": 24086, "市中心医院": 9683, "市中医院": 4377, "妇幼保健院": 7139, "重竞技": 67, "跳水馆": 110, "武术馆": 207, "早间": 1297, "晨练": 2036, "秦琼": 1359, "李炳南": 153, "罗干": 373, "当街": 1841, "邓文迪": 1016, "默多克": 1748, "韩美林": 249, "王玉梅": 108, "任达华": 3441, "张海迪": 596, "姜伟": 483, "孙淳": 390, "岗山": 1049, "王力": 2116, "杜江": 1328, "金铭": 1399, "艳": 37832, "周放": 203, "翟凌": 85, "兽兽": 781, "铁生": 386, "巩晓彬": 196, "韩鹏": 420, "高畅": 104, "张琳芃": 479, "铮": 3919, "中国队": 13283, "多日": 2651, "留言": 33212, "和歌山": 2362, "萨克拉门托": 1268, "里贾纳": 219, "巴新": 259, "奥格斯堡": 1486, "哈尔科夫": 1043, "维捷布斯克": 337, "东爪哇省": 170, "卡赞": 472, "萨博": 1784, "握奇": 93, "公孙弘": 273, "阵图": 1195, "开篇": 5790, "为奇": 864, "奇正": 512, "逮": 3426, "库珀": 2481, "欣喜": 5595, "标准版": 5251, "编好": 725, "划线": 6666, "抢手": 2668, "台面": 10183, "框中": 10315, "关键词": 33306, "有帮助": 60528, "复选": 4632, "中才": 1783, "取回": 10113, "留恋": 6702, "初阶": 657, "培养成": 1872, "烦人": 7760, "欢心": 3100, "初值": 1374, "面目全非": 2126, "向导": 10501, "菜鸟": 15095, "装下": 2232, "伎俩": 2688, "黄粱一梦": 266, "刻舟求剑": 569, "狐假虎威": 808, "愚公移山": 1265, "纸上谈兵": 2218, "完璧归赵": 613, "乐不思蜀": 613, "功德无量": 667, "故弄玄虚": 1099, "老骥伏枥": 481, "青出于蓝": 1179, "囫囵吞枣": 764, "尘埃落定": 2075, "火中取栗": 174, "空穴来风": 1263, "人定胜天": 552, "脚踏实地": 4860, "天花乱坠": 1817, "螳螂捕蝉": 351, "情投意合": 1252, "三番五次": 835, "国泰民安": 769, "动宾": 529, "宾语": 11256, "大显身手": 838, "顾全大局": 781, "偏正": 585, "定语": 9518, "状语": 6637, "世外桃源": 2626, "一盘散沙": 824, "默默不语": 90, "擦肩而过": 3570, "轻于鸿毛": 350, "遗臭万年": 379, "马到成功": 825, "递进": 1959, "温故知新": 377, "虎穴": 985, "焉得": 551, "虎子": 1648, "某事": 4756, "守株待兔": 1277, "插翅难飞": 179, "胡说八道": 5175, "大大咧咧": 2986, "句中": 3057, "千山万水": 1330, "灯红酒绿": 1035, "熟语": 482, "欢天喜地": 2878, "见怪不怪": 1615, "见惯": 771, "凝炼": 435, "无意中": 11199, "前教育部长": 50, "三只": 19193, "小猪": 234279, "杜氏": 1090, "语料": 724, "并无不当": 1105, "懒散": 3831, "职掌": 606, "拟成": 58, "全等": 1397, "诸夏": 450, "外夷": 184, "自居": 3061, "蒋友仁": 31, "高水准": 1943, "一凡": 1805, "生地": 4342, "须持": 952, "走来": 9702, "西藏人": 458, "跟着": 80441, "重做": 7585, "宁可": 9705, "等相": 486, "转口贸易": 362, "一中一台": 172, "美龄": 461, "此消彼长": 767, "决议文": 364, "主流派": 286, "罗志祥": 7045, "黄安": 2483, "轰": 14454, "葡人": 468, "学联": 1433, "跌势": 1083, "五成半": 158, "仅余": 944, "一成一": 71, "有得": 5920, "拣": 4895, "李小龙": 16752, "新加坡人": 1253, "问道": 14807, "文绉绉": 345, "争光": 2123, "入列": 676, "赢政": 380, "张角": 1673, "刘勰": 598, "宗喀巴": 770, "郑燮": 354, "高俅": 1094, "秦淮": 2702, "大陆人": 4731, "称心": 1406, "采自": 836, "印本": 772, "旁征博引": 548, "论兵": 224, "笔录": 1235804, "则士": 275, "循序渐进": 9492, "兴废": 805, "术法": 411, "灿然": 394, "垂范": 138, "攻守": 2493, "对阵": 19028, "阵式": 642, "下卷": 782, "别具一格": 2257, "三原": 4439, "舅舅": 17174, "郡丞": 228, "拥兵自立": 42, "伪装成": 4243, "囚徒": 3602, "密告": 304, "兵荒马乱": 633, "大叫": 7040, "恩怨": 5566, "充做": 52, "投效": 366, "李孝恭": 99, "李瑗": 56, "湖北襄阳": 541, "辰州": 632, "沅陵": 1013, "夏口": 460, "涨": 68088, "湖北宜昌": 740, "顺流": 1450, "官拜": 816, "转任": 5683, "大总管": 849, "冒雪": 342, "黑夜": 13107, "颉利": 191, "步卒": 162, "轻骑": 3751, "威振": 136, "犯境": 145, "请缨": 815, "契苾": 84, "追歼": 149, "大亮": 747, "追截": 277, "追至": 6002, "七十九": 785, "陪葬": 2018, "圆锥形": 1480, "广和": 748, "硖": 811, "威公": 131, "韩氏": 1124, "骠骑": 2446, "新义": 617, "李端": 503, "李正明": 12, "左卫": 2958, "光禄大夫": 2552, "謇": 536, "延州": 421, "清阳": 378, "其女": 1513, "李芳": 1466, "齐贤": 1468, "嘉州": 644, "李彦": 1354, "虬髯客": 503, 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"习惯性": 10946, "菲达": 290, "工矿业": 117, "褐煤": 590, "铝土": 188, "侨汇": 203, "轨距": 2530, "伊斯坦布尔": 3605, "水翼船": 207, "比雷埃夫斯": 210, "元老院": 2425, "德制": 254, "各行各业": 5138, "盖在": 2923, "檐部": 140, "帕德嫩": 188, "柱身": 215, "粗短": 758, "下端": 4359, "扁圆形": 538, "厚实": 7134, "楣梁": 31, "柱形": 1105, "雅典卫城": 413, "修长": 5370, "垂直线": 715, "涡卷": 147, "轻灵": 653, "成所": 280, "代之以": 929, "繁密": 266, "重迭": 235, "华美": 6300, "雅典大学": 83, "希腊议会": 146, "东光县": 1639, "赵奢": 430, "皇甫嵩": 355, "朱隽": 64, "徐晃": 1331, "陆抗": 364, "羊祜": 527, "杜预": 624, "陶侃": 700, "王猛": 1444, "王镇": 1389, "李抱真": 54, "王忠": 1016, "王绩": 342, "绛州": 553, "龙门县": 1741, "诗坛": 1334, "独来独往": 1743, "冠冕": 1098, "家富": 456, "游长安": 77, "大出风头": 221, "五斗": 1426, "高悬": 1034, "风疾": 185, "轻舟": 1721, "遁": 8922, "辞官": 1712, "旧官": 93, "王静": 842, "答曰": 2717, "殊为": 252, "萧瑟": 2654, "酝": 1043, "差可": 291, "自求": 333, "好酒": 2802, "长叹": 1638, "美酒": 6330, "自号": 1845, "第一百二十": 790, "铝硅酸盐": 130, "朗斯": 1993, "灼烧": 2574, "沸腾": 10890, "含水量": 5594, "加热": 60489, "空腔": 1772, "浅色": 8693, "孔隙": 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"父兄": 1339, "广东佛山": 1055, "梅村": 2048, "硬功": 250, "五枚": 1087, "设帐": 70, "鹞子": 335, "洪家拳": 38, "林至善": 8, "张炎": 295, "不靖": 73, "富贵人家": 506, "武师": 745, "护院": 724, "家仆": 310, "自设": 785, "一口气": 19046, "旭东": 2217, "真传": 1803, "家法": 784, "其师": 740, "荣生": 620, "抄写": 3452, "义和拳": 203, "借镜": 181, "天铎报": 9, "陈铁生": 19, "绷紧": 2340, "丹田": 2838, "肚脐": 7765, "腹肌": 14739, "柔顺": 4763, "强毅": 55, "同道": 2349, "研究社": 1966, "志云": 546, "纪子": 2082, "克兴": 154, "瑞甫": 20, "转形": 107, "桥名": 240, "斋主": 283, "拟似": 230, "谔": 6390, "精义": 408, "实与": 315, "武术界": 340, "源出": 693, "峨嵋": 2474, "万年青": 1938, "乾隆皇": 270, "半点": 6123, "现称": 1502, "强种": 48, "编审": 1773, "张之江": 99, "杜心": 286, "掌门人": 2379, "蛾眉": 1412, "个个": 13095, "御侮": 233, "图强": 1018, "手抄": 5149, "秘本": 139, "发生关系": 7219, "金恩": 1163, "秘传": 1052, "绝技": 5617, "点穴": 1653, "武术队": 203, "备要": 280, "不前": 2025, "河北人": 937, "二十四式": 102, "扇舞": 165, "剑舞": 1980, "健身操": 8928, "国家体委": 447, "表演赛": 1214, "竞逐": 2104, "赛制": 4549, "筹委会": 970, "武术院": 28, "国际武联": 58, "吸收为": 277, "滴水": 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"看管": 13827, "感时": 908, "哀江头": 43, "麻鞋": 120, "郧": 1037, "所感": 1429, "三吏": 206, "弃官": 752, "备尝艰辛": 48, "绝境": 7344, "缺衣少食": 145, "饥寒交迫": 691, "悄": 3650, "仰视": 2107, "浣花溪": 293, "阆州": 323, "醉酒": 471176, "救下": 2508, "暮春": 1442, "旰": 473, "病休": 486, "沙鸥": 1065, "五十九岁": 133, "太甚": 293, "消渴": 2119, "食尽": 242, "大饱": 63, "食物中毒": 4698, "平江县": 4174, "三十五岁": 987, "山东北部": 84, "际遇": 1887, "坎坷": 8594, "困居": 45, "四十四岁": 206, "悲辛": 232, "丽人": 8189, "四十八岁": 260, "把家": 2363, "月夜": 5088, "春夜": 1270, "喜雨": 1402, "登楼": 1819, "秋兴": 773, "可厌": 175, "特多": 1441, "仁民爱物": 32, "苦乐": 2199, "寄情": 1219, "入诗": 414, "多姿多采": 87, "声律": 680, "顿挫": 1761, "阔大": 433, "幽思": 374, "丽日": 703, "淆乱": 50, "横行霸道": 914, "悲悯": 1274, "举上": 71, "下句": 1792, "满天下": 594, "风骚": 4065, "孤高": 1025, "流丽": 153, "高妙": 557, "逸之": 300, "气包": 119, "丽之": 307, "狂放": 1145, "豪气": 2153, "僻": 2901, "光焰": 361, "万丈": 3237, "巨细": 477, "巍峨": 2092, "幻化": 2606, "精到": 539, "白鸟": 3401, "萧萧": 6258, "禄山": 273, "史证": 104, "悲天悯人": 702, "杨亿": 88, "刘放": 220, "大年": 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"渔家傲": 637, "晓雾": 266, "清平乐": 632, "猊": 938, "减字": 323, "担上": 162, "孤雁": 1282, "钓台": 770, "后序": 483, "崇礼": 1488, "开首": 654, "点点滴滴": 4505, "笔误": 1005, "确是": 4538, "窗儿": 182, "怎生": 405, "张瑞": 1295, "先得": 2891, "唐宋词": 199, "叹赏": 91, "自愧": 452, "谢客": 343, "忌食": 7355, "绝佳": 6142, "消魂": 600, "道出了": 2225, "蓼": 4600, "煞是": 609, "妙绝": 304, "谩": 3785, "鬼雄": 468, "玉堂": 3305, "清照": 361, "柳浪闻莺": 278, "王光": 1483, "清风明月": 611, "朗诺": 209, "噩": 1923, "官家": 1211, "贫无立锥": 15, "翰墨": 1687, "情场": 2203, "生新": 512, "清幽": 1913, "暗香": 4066, "疏影": 2866, "飞去": 4532, "白雨斋": 54, "清真词": 23, "推词": 17, "善自": 78, "新腔": 71, "宋韵": 54, "徐玮": 168, "画道": 30, "悟到": 768, "矢志": 597, "二十九岁": 398, "埋首": 224, "饱食终日": 141, "丝衣": 60, "饲育": 533, "赶考": 766, "教谕": 1264, "病根": 1781, "遇合": 217, "奇事": 1110, "疾书": 136, "汀州府": 195, "潘吉星": 24, "本村": 27093, "着作": 719, "画音": 28, "观象": 239, "薜": 1001, "尤物": 1690, "躁动": 2784, "满腔热忱": 125, "泰山北斗": 153, "烈山": 729, "火耕": 105, "九黎": 497, "记载下来": 246, "貔貅": 11310, "刑天": 2730, "渴死": 685, "后土": 911, 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5092, "轰轰": 1178, "刘怀": 256, "坏坏": 2203, "还还": 773, "乱乱": 806, "断断": 254, "动动": 3001, "搀": 1086, "馋": 5620, "壶壶": 1414, "显显": 83, "帅帅": 2480, "边边": 839, "牵牵": 371, "乔乔": 1591, "养养": 626, "陆陆": 205, "乡乡": 488, "开开关关": 94, "凿凿": 159, "号号": 325, "厌厌": 386, "压压": 547, "赶赶": 65, "扫扫": 325, "椭": 1084, "疖": 751, "杀杀": 676, "灭灭": 180, "运运": 128, "华华": 837, "毕毕": 58, "进进": 400, "阳阳": 1846, "响响": 86, "纟": 301, "左旁": 82, "辔": 1975, "飧": 529, "在手": 5015, "曹伯": 84, "忾": 153, "气气": 397, "叽叽": 1709, "虮": 410, "蛏": 803, "淋漓": 3141, "离离": 2049, "咫": 515, "分出来": 1401, "弹弹": 2625, "冁": 79, "苋": 1398, "攵": 209, "熠": 1470, "尔尔": 293, "头头": 2138, "页页": 241, "颔": 2269, "顺顺": 1235, "转转": 8695, "广被": 252, "增入": 91, "猎猎": 952, "鬣狗": 1941, "鬣": 1174, "难难": 377, "瘫": 7862, "滩滩": 47, "拣拣": 18, "练练": 7151, "丬": 69, "柠": 4224, "此表": 878, "游游": 537, "够够": 672, "凉凉": 5806, "凄凄": 1407, "强强": 3123, "泛泛": 2465, "喂喂": 9078, "撑撑": 139, "棱棱": 85, "痴痴": 1692, "哄哄": 3725, "稭": 46, "杰杰": 1150, "捆捆": 16, "脉脉": 1686, "凶凶": 258, "岳岳": 290, "袅": 1555, "温温": 861, "媪": 722, "嘘嘘": 1292, "睁睁": 23, "黄黄": 2481, "禄禄": 29, "秃秃": 208, "转繁": 34, "手书字": 24, "招贴": 397, "苏培": 294, "滑翔机": 1686, "冲压": 4615, "乐亭县": 1927, "穿堂": 549, "石驸马": 15, "新文化街": 47, "三十二万": 81, "法拉弟": 45, "政闻社": 27, "铃木镇一": 34, "营养学家": 1627, "减肥法": 4894, "地震学家": 304, "李艳秋": 173, "敏儿": 576, "怀克里夫": 39, "声武": 53, "欠藏": 3, "拍摄者": 1033, "小芹": 334, "今井": 2256, "太空管理局": 3, "普特尼": 188, "加洲": 336, "克莱门汀": 234, "侦察兵": 2371, "鬼方": 227, "甘泉": 3865, "魏纳": 33, "延安城": 50, "衔尾": 242, "赫连勃勃": 380, "五万三千": 59, "九百三十九": 10, "延川": 1339, "文安": 1367, "丹州": 186, "武东": 699, "富州": 234, "保安军": 33, "志丹": 463, "绥德": 1915, "清涧": 1602, "官厂": 54, "延长县": 734, "吴旗": 86, "鄜": 250, "洛川县": 794, "黄龙县": 352, "宜川县": 1400, "铜川": 1528, "猴儿": 566, "梁峁": 36, "塬": 2246, "大墩": 682, "二十里铺": 217, "西铁": 3743, "爆炸品": 703, "首列": 649, "延安北站": 11, "长庆油田": 719, "延水": 85, "宝塔山": 272, "成材": 1566, "本城": 929, "缸内": 2283, "堤道": 587, "赃物": 163133, "度假地": 335, "炼焦煤": 135, "装机": 10109, "叙事曲": 425, "马祖卡": 67, "缪塞": 89, "第一眼": 7559, "大家闺秀": 1060, "颞叶": 2585, "玛祖卡": 218, "中听": 791, "五度音": 83, "团团转": 3544, "诙谐曲": 106, "喘不过": 3151, "气来": 2535, "斯克里亚宾": 123, "第四十四": 3691, "于部": 55, "不净观": 185, "数息观": 50, "根性": 968, "对治": 733, "慧观": 72, "瑜伽师地": 388, "安那般那念": 27, "不净": 2308, "定数": 1426, "中阿含": 230, "复修": 629, "我慢": 869, "无量心": 191, "自灭": 276, "生法": 369, "瞋": 1096, "愚痴": 859, "瞋怒": 16, "杂阿含": 561, "杂心": 40, "愚夫": 243, "思惟": 907, "障蔽": 68, "慧眼": 2158, "善知识": 909, "此身": 2291, "自性": 3238, "水界": 90, "相离": 1197, "积沙": 149, "如是观": 95, "得空": 532, "乐得": 1359, "念佛观": 33, "心绪": 1740, "嗔": 3273, "散乱": 2142, "数到": 1882, "散心": 1690, "我执": 1276, "寂灭": 900, "实相": 1547, "藉诸": 5, "体性": 388, "苦行林": 6, "觉悟到": 148, "缘起法": 131, "续缘": 322, "心说": 830, "三生": 7843, "十四五": 1031, "十八九岁": 441, "尘境": 125, "大分": 1954, "心心": 1962, "染污": 753, "驰求": 27, "业识": 78, "外境": 401, "般若波罗蜜": 1427, "空无所有": 80, "老病": 1242, "戏论": 152, "求诸": 349, "但求": 2341, "忧怖": 18, "老法": 122, "虚妄": 1796, "因缘法": 98, "义道": 135, "九八": 2003, "载乘": 3777, "军事侦察": 71, "救生舱": 134, "舱段": 189, "作何": 1820, "相依为命": 2513, "设限": 1071, "主调": 755, "轰然": 977, "悲愁": 353, "奥杰塔": 33, "跃入": 515, "妖艳": 2458, "天之骄子": 872, "托起": 1790, "重头戏": 1907, "魏泽尔": 52, "换钱": 2291, "写些": 1577, "波尔卡": 1127, "意料中事": 57, "料想到": 191, "兰奇": 1717, "吉赛尔": 458, "土番": 97, "屈就": 266, "保人": 659, "克尔克岛": 17, "齐尼": 397, "斯夫": 303, "科托尔湾": 36, "质子号": 121, "反作用力": 1492, "喷管": 1198, "奥伯特": 143, "入轨": 475, "入时": 696, "气动力": 346, "钝形": 84, "隔热板": 274, "亚音速": 1061, "场地费": 783, "生命攸关": 66, "干扰到": 514, "磁暴": 662, "外空": 526, "向心": 1107, "航次": 572, "连绵不绝": 475, "尘粒": 386, "焦仁和": 64, "蹉商": 18, "国安会": 334, "王在希": 28, "台方": 367, "来函": 597, "确已": 3656, "唐树备": 55, "张志军": 328, "慎用": 5723, "梅里特": 189, "燃料箱": 488, "门板": 3510, "抽干": 634, "载物": 2501, "立升": 443, "震破": 167, "卸下来": 1213, "入门票": 20, "展览品": 220, "墙里": 1265, "接回来": 871, "格里森": 359, "登出": 1233, "丧身": 237, "防热": 436, "台顶": 144, "夯筑": 223, "砌砖": 699, "地平": 3432, "象限仪": 146, "纪限仪": 24, "地平经纬仪": 11, "抚辰仪": 10, "八件": 678, "台元": 144, "恂和": 5, "监正": 82, "台边": 323, "地平经仪": 9, "地平纬仪": 7, "桔园": 663, "南运": 325, "陈中": 878, "西谛": 27, "郭源新": 3, "小说月报": 325, "文协": 257, "中央文化部": 33, "全委": 149, "朴社": 51, "图录": 610, "桂公塘": 9, "良友": 1051, "民族文": 28, "散记": 358, "蕴华": 150, "血痕": 946, "铁流": 263, "编作": 159, "慎子": 94, "提价": 1959, "大财": 4228, "公信": 1071, "偿": 5089, "囤积居奇": 189, "基准价": 444, "刑鼎": 38, "苦生": 69, "封泥": 136, "督责": 106, "竹间": 647, "毋宁": 326, "设局": 1008, "两可": 476, "郑延平": 44, "灰暗面": 24, "浅陋": 408, "刻薄者": 6, "陈弘毅": 77, "弹拨乐": 86, "箜篌": 641, "中阮": 364, "大阮": 112, "柳琴": 340, "高胡": 381, "板胡": 338, "南胡": 137, "中胡": 479, "革胡": 87, "椰胡": 56, "排箫": 433, "梆笛": 66, "编磬": 107, "小堂鼓": 15, "鼓大": 78, "鼓缸": 3, "战鼓": 1512, "板鼓": 71, "扬琴": 1160, "小钹": 33, "从价": 444, "导带": 385, "载流子": 1111, "够高": 1683, "磷化": 8739, "跳入": 3495, "色散": 2332, "让价": 74, "孪": 1256, "堆垛": 507, "美光": 948, "葛罗斯": 157, "演奏出来": 154, "大事录": 43, "训诲": 148, "安提阿": 454, "康士坦斯": 31, "大公教会": 183, "徒长": 1196, "终身职": 99, "七机部": 84, "工办": 159, "科技处": 277, "联络部": 370, "市委组织部": 948, "反戈一击": 189, "江派": 61, "捞取": 468, "巍峙": 64, "排解": 2619, "干爹": 3010, "裸官": 452, "官运": 601, "头面人物": 154, "山雨欲来": 430, "腐化堕落": 290, "蹿升": 248, "进村": 1943, "打枪": 1363, "唯唯诺诺": 1020, "红灯": 14505, "王凤清": 9, "后台老板": 75, "庆源": 325, "保障部": 919, "晓玲": 674, "生活会": 3528, "几百家": 460, "上周": 21153, "肖建华": 46, "国安部": 202, "港媒": 859, "绒毡": 111, "木槌": 299, "宏亮": 635, "气势磅礡": 69, "小臂": 2861, "皮制": 519, "拨子": 341, "拨动": 2204, "击弦机": 125, "弹开": 1098, "弹动": 147, "延音": 303, "音栓": 55, "制音器": 35, "白键": 321, "黑键": 549, "运给": 113, "制音": 80, "顿音": 52, "直立式": 277, "立式": 3898, "音板": 235, "弦音": 384, "棰": 336, "谱表": 205, "单音": 877, "踩下": 1929, "琴音": 930, "强音": 507, "踏下": 349, "绒布": 1797, "隔在": 227, "声量": 768, "掣": 1681, "贝森朵夫": 93, "番地": 773, "史坦威": 74, "金牌奖": 211, "电吉他": 2941, "木箱": 3563, "箱体": 3400, "锲而不舍": 1702, "日积月累": 3280, "尊君": 150, "郢城": 137, "伯韩": 35, "仁智": 377, "狂妄自大": 851, "礼义廉耻": 479, "乡俗": 108, "潜在于": 28, "拟作": 343, "不恶": 1026, "自私自利": 2234, "言过其实": 902, "合群": 2291, "舍生取义": 409, "逆水行舟": 564, "静心": 4142, "习惯成自然": 661, 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"肖望东": 28, "于会泳": 37, "局长级": 55, "查良镛": 310, "推敲出": 64, "陈近南": 469, "少奶": 224, "毛东": 122, "弄致": 32, "鹿鼎": 188, "通吃": 2743, "吴六": 148, "闲得": 1391, "自查": 2804, "剪碎": 475, "砌好": 259, "大中国主义": 16, "藏诸": 83, "打足": 224, "小算盘": 462, "步进": 3007, "写尽": 520, "坑蒙拐骗": 1127, "卡列宁": 154, "正牌": 2263, "吴雪": 300, "梦回": 5088, "叶韵": 66, "友荣": 55, "黄启明": 61, "林司": 201, "建良": 842, "伟力": 368, "振邦": 518, "吴大鹏": 19, "老本": 914, "淡月": 696, "漫画区": 35, "横置": 548, "上缘": 1068, "半侧": 379, "鼓包": 1567, "泄压": 1216, "引擎室": 271, "右后": 5852, "四成五": 35, "广告学系": 61, "翠华路": 213, "宏路": 1632, "选线": 515, "万方数据": 126, "徐冠华": 66, "公使衔": 110, "博华": 300, "密执安": 37, "占民": 48, "交通厅": 666, "杜传": 46, "阜东": 242, "大丰县": 60, "射阳县": 3676, "滨海县": 7170, "建湖": 570, "原盐": 287, "强降雨": 1218, "水绿": 478, "射阳河": 65, "东台市": 5098, "收录机": 311, "悦达": 539, "悦达起亚": 1271, "大丰港": 141, "串场": 585, "中评委": 68, "亚文": 4840, "九龙口": 209, "大纵湖": 156, "护盖": 189, "过劲": 109, "浑名": 78, "柯夫": 488, "公司董事会": 1340, "跑单帮": 26, "整编成": 65, "发直": 254, "刻纹": 313, "长方型": 206, 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415, "九江站": 120, "运能": 598, "马草河": 7, "大京九": 33, "广安门外": 191, "至乐": 811, "涵洞": 3208, "里程表": 1168, "仄声韵": 45, "何妨": 4238, "芒鞋": 611, "料峭": 464, "微冷": 202, "放花": 663, "冉冉": 2052, "彩笔": 1957, "新题": 602, "弄影": 564, "自落": 444, "计得": 144, "伊书": 11, "吹皱": 206, "搔头": 357, "娇笑": 94, "接水": 1389, "柳飞": 84, "终须": 885, "缠绊": 7, "翠色": 571, "竹篱": 493, "酒旗": 609, "芦叶": 240, "新愁": 534, "淹留": 950, "飕飕": 533, "且住": 222, "相如赋": 90, "只影": 408, "荒烟": 291, "落拓": 335, "裛": 254, "烟合": 62, "寒声": 544, "垂地": 254, "练长": 45, "递转": 14, "雪晴": 751, "同路": 1149, "名调": 44, "忍泪": 193, "暗杀案": 30, "蒂利希": 34, "右脸": 1417, "转过去": 1408, "收刀": 183, "入鞘": 110, "淫威": 631, "补偿费": 6386, "拆信": 117, "卷进": 746, "亲如手足": 77, "光芒万丈": 694, "土财主": 175, "批判声": 21, "环卫工": 1229, "走进去": 1634, "枪筒": 100, "完蛋": 6996, "哈里斯堡": 188, "最近点": 219, "南竿岛": 75, "北竿岛": 52, "高登岛": 29, "白眉燕鸥": 23, "红燕鸥": 29, "苍燕鸥": 20, "湄洲": 548, "马祖人": 33, "马港": 267, "乡林": 360, "津沙": 38, "北茭": 27, "锥状": 516, "卵石滩": 27, "继光饼": 11, "笔架": 458, "鱼面": 310, "陈高": 166, "马祖酥": 6, "芙蓉酥": 6, "烤熟": 1723, "摘采": 59, "塘岐": 40, "芹壁村": 27, "沃口": 13, "官室": 76, "射口": 63, "装设有": 124, "牛角村": 63, "牛角岭": 17, "显目": 130, "檐上": 161, "圆拱形": 137, "灯罩": 2140, "风标": 307, "九点五": 67, "六一五": 18, "青帆": 20, "慑人": 160, "雾季": 34, "南北竿": 22, "关场": 29, "报核": 36, "东西莒": 4, "这阵": 379, "企划案": 243, "早餐店": 3207, "陈春梅": 14, "烟硝": 48, "复兴村": 403, "令旗": 230, "除秽": 68, "好词": 2439, "暗想": 480, "撩乱": 403, "凉气": 971, "斜风": 279, "歌酒": 243, "伤怀": 609, "孤坟": 551, "娉婷": 849, "湘灵": 223, "寻问": 373, "碧野": 165, "伯恩哈特": 108, "验证为": 130, "高眠": 332, "牌名": 202, "冰肌玉骨": 174, "疏星": 239, "岁晚": 1735, "雨歇": 458, "望眼": 636, "仰天长啸": 456, "壮怀激烈": 203, "悲切": 698, "长车": 219, "踏破": 1367, "珠翠": 499, "壮怀": 273, "消歇": 124, "敲碎": 2959, "妾身": 499, "窗纱": 1110, "楼头": 702, "急风": 145, "林声": 93, "新云": 404, "晓色": 456, "深幽": 291, "微利": 1372, "些子": 238, "相妨": 136, "说短论长": 17, "云幕": 125, "天香国": 76, "安枕": 199, "苒": 1247, "楼长": 327, "归帆": 297, "矗": 1853, "生香": 2256, "切画": 5, "下窥": 80, "分种": 695, "兰芷": 226, "季鹰": 167, "航发": 543, "公秉": 119, "话机": 872, "改出": 380, "倒飞": 160, "三机": 311, "试飞组": 7, "拖走": 1990, "料件": 428, "待修": 213, "网膜": 1504, "飞安": 264, "飞脱": 95, "数授": 4, "尸块": 1057, "台商会": 50, "二十九号": 143, "联信": 275, "机棚": 83, "喷在": 1579, "喷上": 1725, "飞行官": 45, "毕世": 54, "拉升": 3988, "碰碰": 2264, "研发案": 23, "删砍": 3, "寻标器": 115, "陈正兴": 13, "翔翔": 401, "吊挂": 1064, "垫高": 1904, "乐观其成": 104, "控制律": 319, "预订量": 225, "六十公里": 83, "台机": 2524, "前视": 298, "侧视": 315, "台翔": 617, "运交": 134, "轰六": 221, "张家豪": 103, "海翔": 427, "张野": 140, "凄切": 488, "催发": 373, "暮霭": 485, "吹去": 557, "霜鬓": 432, "鱼圆": 218, "圆谎": 285, "乔杰立": 226, "孙德荣": 120, "仄声字": 79, "世路": 868, "袖手": 671, "跃出": 732, "乳燕": 317, "求名": 750, "高难": 561, "代书": 1381, "命薄": 308, "一诺": 1408, "深恩": 279, "长辞": 95, "摧折": 302, "心魂": 325, "眉梢": 685, "前番": 99, "孤旅": 107, "恰像": 29, "应收": 10356, "话务量": 161, "国际标": 23, "揽下": 218, "重起炉灶": 43, "超轻型": 276, "内倾": 498, "范保罗": 23, "风挡": 2542, "吊舱": 1090, "施洒": 7, "热焰": 88, "缅甸空军": 63, "雪泥": 310, "三十八万": 79, "石骨": 104, "自然铜": 100, "三千两百": 33, "超人类": 317, "解释解释": 709, "折旧费": 987, "权益法": 695, "到期日": 2355, "摊销": 4483, "金融业者": 67, "勃然": 181, "开口处": 845, "固定栓": 41, "垫板": 735, "驰行": 25, "盖马": 91, "刺吏": 33, "苍岩": 74, "投靠到": 47, "窃盗罪": 69, "迎出": 32, "神象": 225, "蒙立": 6, "相悦": 320, "男家": 493, "东昭": 21, "乡曲": 355, "厚意": 205, "男相": 183, "治治": 573, "四百七十": 97, "国巨": 80, "十五集": 1781, "公辽": 19, "阿苏火山": 42, "高感染率": 13, "澳人": 78, "亚杰": 341, "赫尔丁": 40, "孟加拉虎": 508, "杰沙": 198, "每日星报": 107, "乡村路": 291, "孟军": 109, "阿扎姆": 109, "战斗性": 213, "黄苏": 97, "冼妮娜": 6, "珊瑚群": 54, "散见": 189, "一亿七千": 22, "三亿六千万": 23, "二十九亿": 8, "解难": 893, "目录表": 266, "霸王龙": 5643, "宽街": 341, "德辅": 105, "豪斯曼": 159, "位移民": 45, "马林鱼": 673, "李廷珪": 49, "龙尾砚": 50, "入文": 77, "侯人": 43, "西塞山": 1015, "松鼠桂鱼": 110, "既订": 6, "嘿嘿": 34573, "克朗凯": 13, "站票": 1196, "余登发": 147, "梅度": 65, "柳比西奇": 65, "张海丽": 74, "周丽淇": 429, "帕布洛": 56, "艾马尔": 156, "奥多姆": 2275, "翼鹏": 34, "卢致德": 16, "毕肖普": 92, "宗教画": 103, "王子公园": 120, "佳林": 295, "连续式": 387, "进尺": 181, "浮船坞": 50, "硅酸钙": 350, "铜屑": 167, "闻出": 313, "水槽": 5376, "李伟文": 73, "奈伊": 294, "六氟化铀": 106, "开采出": 211, "马格拉夫": 13, "蒙乃尔": 93, "四氟化铀": 26, "焊条": 2800, "玻纤": 1154, "防护服": 821, "氟乐灵": 51, "氟乙酸": 416, "酞": 1252, "可的松": 1065, "地塞米松": 2078, "吸入型": 36, "乳状液": 259, "鼠子": 81, "钙血症": 413, "截除": 130, "尿检": 14176, "珠江口盆地": 43, "红潮": 453, "红黄绿": 391, "绿潮": 68, "微量性": 3, "谷皮": 88, "脚气病": 1128, "癞": 1072, "神经炎": 1693, "凯氏": 329, "脂溢": 5840, "青春痘": 9724, "刺麻": 21, "不育症": 945, "硫氨基酸": 326, "万艾可": 1358, "电控": 3169, "谢婉莹": 213, "贝满": 63, "斯人独憔悴": 78, "心功能": 2508, "宗远": 73, "吴青": 229, "教子无方": 45, "枉为": 229, "受试": 479, "接反": 790, "动作量": 13, "语尾助词": 53, "成熟化": 126, "头毛": 166, "扁柏": 205, "压花": 674, "招弟": 115, "直直": 1413, "泛词": 11, "讨海人": 32, "手路菜": 10, "脚指": 354, "树子": 920, "跤手": 28, "衣裤": 3449, "钱儿": 659, "青红灯": 24, "草寮": 89, "工寮": 86, "集合处": 16, "伊们": 63, "乌鸦嘴": 491, "湿湿": 1744, "烧烧": 459, "热热": 1555, "红红的": 5446, "莽莽撞撞": 79, "夭寿": 285, "雄雄": 937, "险险": 99, "离离落落": 3, "零零落落": 90, "黑漆漆": 833, "完成式": 391, "动词组": 36, "下轿": 136, "流莺": 542, "踽": 35, "时态助词": 27, 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565, "李居明": 231, "正点": 1532, "前度": 1426, "伟伟": 1138, "罗羽": 67, "彭诗婷": 7, "金亮": 552, "农科院": 937, "禁刊": 9, "二五六": 79, "贾尼斯": 78, "解囊相助": 60, "罗燕": 133, "婉秋": 191, "郑震": 17, "木鸡": 180, "异乡客": 80, "舞艺": 188, "屈伸": 2059, "赵维": 128, "作为客体": 45, "啤酒罐": 189, "潜水衣": 149, "液态氩": 14, "保二": 135, "汤二": 104, "胜部": 102, "左君": 22, "从东路": 18, "谢再兴": 30, "分坛": 60, "坛主": 162, "大园": 1188, "郑经改": 12, "齐开": 168, "客家籍": 75, "绷绷": 55, "西丘": 97, "雪白山": 9, "窄狭": 76, "农渔牧": 30, "老街溪": 95, "外凸": 530, "样区": 48, "国有林": 1202, "红桧": 137, "栖兰": 69, "义民爷": 74, "毛猪": 261, "洋香瓜": 67, "染整业": 11, "净水场": 113, "童玩": 225, "八音团": 9, "陈欣慧": 2, "郑焕": 72, "新莲": 274, "亚君": 163, "蔡诗萍": 48, "古蒙仁": 21, "协拍": 100, "吴炫三": 36, "主展场": 11, "总务组": 126, "特展室": 25, "射箭场": 161, "立青": 175, "少棒赛": 58, "扬升": 397, "趋时": 157, "育达": 261, "挤破": 1475, "招满": 445, "国中小": 181, "高职部": 186, "中坜站": 48, "移用": 113, "一轻": 200, "小乌来": 72, "东眼山": 51, "吕宅": 60, "观光化": 29, "红曲": 1270, "软糖": 1495, "黑心肝": 23, "礼盒": 6229, "良质": 195, "找碴": 245, "茶精": 25, "桃园站": 362, "中平路": 57, "台茂": 80, "供应器": 515, "书法课": 218, "萧淑芳": 21, "谢稚柳": 62, "继高": 105, "雕塑展": 85, "吕霞光": 4, "李瑞年": 9, "油画展": 188, "米开兰基罗": 14, "诀窍": 5729, "裸体画": 127, "私处": 2226, "曼坦那": 2, "乔尔乔内": 61, "俗丽": 50, "除旧布新": 157, "稳恒态": 59, "法国罗浮宫": 22, "粉色调": 90, "一七二": 73, "绽": 2360, "悠闲地": 887, "小大人": 923, "寒害": 147, "岩浆流": 47, "山泥": 247, "淹埋": 52, "尼欧斯": 29, "莫瑙恩": 16, "雪暴": 153, "余辉": 658, "臣仆": 271, "颈脖": 182, "搔挠": 20, "转转向": 12, "擦抹": 426, "擦净": 1187, "连根拔": 173, "余烬": 378, "英语班": 585, "卡片式": 388, "活动周": 304, "期中考试": 2234, "社团部": 70, "墨笔": 198, "电脑社": 27, "礼仪社": 14, "出乎意外": 241, "桑妮雅": 25, "聚到": 492, "钢化玻璃": 4170, "落地窗": 1464, "主厅": 195, "分业": 333, "补充品": 419, "脱发": 29411, "巡房": 53, "配得": 815, "药单": 161, "髂": 4071, "正中线": 437, "附面": 224, "并行计算机": 107, "气液": 942, "制作器": 151, "郑凤荣": 60, "阿吉仔": 249, "德沃特": 218, "吹田市": 151, "宣萱": 1448, "伊东市": 40, "品脱": 378, "棒总": 23, "徽音": 246, "建国西路": 235, "别墅型": 49, "蓊": 237, "泛黑": 93, "果粒": 1394, "无痰": 1041, "蜡丸": 109, "套袋": 778, "幼果": 513, "抽枝": 132, "春果": 49, "多量": 1439, "保湿": 29009, "间伐": 349, "莲雾园": 6, "介壳虫": 1762, "瘿": 1132, "果形": 401, "白色带": 347, "稍带": 647, "盐份": 751, "疏果": 283, "果柄": 339, "落果": 648, "徒长枝": 314, "古坑乡": 357, "果面": 359, "润肺": 8942, "陈白尘": 61, "小井": 743, "安道尔公国": 60, "西西利亚": 28, "戈尔达": 140, "狭谷": 137, "七人制": 628, "塔兰奇": 33, "无领": 300, "揉揉": 1986, "揉入": 149, "大盘鸡": 1609, "沙湾县": 489, "俄化": 71, "喀克": 39, "椐": 266, "赛乃姆": 97, "十多个小时": 444, "歌舞曲": 127, "刀郎赛乃姆": 3, "呼图壁": 731, "英吉沙": 168, "上期": 3014, "马赫穆德": 119, "廉访使": 65, "舜天": 1164, "国奥队": 934, "阿的江": 235, "迪里拜尔": 57, "热合": 272, "达瓦孜": 34, "吕传胜": 9, "谈起来": 300, "求处": 165, "仁教所": 8, "新潮流": 742, "良心话": 396, "假仙": 197, "救不了": 1980, "亲属编": 92, "冲著": 67, "科什图尼察": 21, "诺维萨德": 91, "下咽": 961, "无防": 314, "当贝": 2813, "马吉欧": 54, "斯泰莱": 69, "安代": 126, "成本价": 3657, "跨越到": 220, "上光": 989, "棉花糖": 9791, "来回票": 109, "日票": 176, "本国籍": 67, "华尔纳": 16, "连跌": 356, "狂跌": 475, "长销": 64, "植牙": 305, "达米特": 17, "餐厅区": 35, "道达尔公司": 27, "参与进来": 1328, "涛涛": 1379, "登盛": 299, "人民院": 233, "全民盟": 53, "钦纽": 44, "貌埃": 10, "克钦族": 100, "高明骏": 155, "不辞辛苦": 466, "巡回医疗": 25, "梁红": 336, "技惊全场": 13, "瓦城": 180, "油饼": 1348, "艺品": 312, "南投区": 40, "草屯镇": 383, "中寮乡": 199, "芬园乡": 242, "杉林溪": 200, "南投县长": 139, "草屯": 865, "撑竿跳高": 174, "丰荣": 437, "名间": 442, "中潭": 186, "玉山线": 182, "奥万大": 57, "惠荪": 78, "丹大林道": 25, "麒麟潭": 23, "石城谷": 2, "松柏岭": 55, "手创馆": 65, "主题馆": 370, "林班": 12443, "刘丽卿": 5, "孔维勤": 3, "单德兴": 12, "熊秉元": 18, "玉秀": 300, "岩上": 538, "李瑞腾": 29, "林源朗": 13, "彭百显": 78, "林宗男": 75, "埔里人": 16, "班级数": 164, "后埔": 743, "秀朗": 205, "渊远": 47, "学求": 63, "传芳": 230, "家树": 251, "霖园": 102, "万财": 56, "苏永钦": 37, "尼龙布": 169, "阿伯拉罕": 109, "百达": 1811, "手帐": 2067, "手饰": 870, "机械表": 3753, "年历表": 50, "飞行表": 28, "哀婉": 366, "神伤": 544, "西仔": 101, "矿工业": 41, "货柜港": 29, "碇内": 42, "玛陵": 62, "情人湖": 96, "信二路": 26, "金山庄": 24, "李进勇": 112, "虎仔": 160, "忠一路": 31, "忠二路": 8, "忠三路": 5, "忠四路": 7, "孝二路": 15, "孝三路": 9, "仁一路": 29, "仁二路": 29, "仁三路": 17, "仁五路": 15, "爱三路": 39, "爱五路": 8, "刘铭传路": 16, "信一路": 14, "信三路": 5, "义一路": 14, "义二路": 14, "陆面": 148, "外木山": 52, "晓日": 753, "吴淑娟": 7, "春安": 203, "陈万军": 6, "晓玫": 468, "简美玲": 3, "学敏": 316, "云霞": 2101, "洪顺": 138, "卢非易": 20, "正滨": 52, "碧砂": 66, "中元祭": 74, "客死他乡": 193, "水灯": 225, "庆赞": 37, "盛产期": 96, "羽球馆": 52, "祥丰": 330, "月眉": 822, "二信": 82, "柏克利": 28, "张正杰": 9, "李逸洋": 54, "卢秀燕": 90, "郭素春": 34, "陈重光": 61, "秀芳": 391, "曾淑贤": 16, "陈怡安": 34, "侦查庭": 33, "民事庭": 195, "工程款": 28243, "五千多万": 95, "乍看之下": 537, "深省": 289, "屯垦区": 172, "私设": 1770, "关说案": 48, "雨点小": 432, "宜市": 63, "陈明文": 190, "嘉嘉": 1206, "恋爱史": 510, "一八七五年": 9, "玉米地": 2295, "五七九": 45, "两地分居": 1265, "养殖户": 2729, "互帮": 860, "菜农": 842, "助残": 580, "排忧解难": 917, "落到实处": 1428, "分片包干": 33, "一结": 317, "脱贫致富": 642, "义务服务": 108, "非遗传性": 134, "近视眼镜": 1011, "近视镜": 567, "配镜": 2455, "龟山里": 37, "二仁溪": 148, "万里乡": 81, "通霄镇": 286, "清水镇": 344, "大里乡": 119, "乌日乡": 63, "口湖乡": 152, "水林乡": 115, "美浓镇": 50, "三民乡": 23, "远视眼": 390, "归人": 889, "商专": 244, "语文系": 125, "华纳威秀": 56, "弹簧床": 296, "写信人": 178, "出人意外": 222, "生物课": 799, "好烧": 194, "说说话": 3627, "守密": 77, "当店": 148, "桶柑": 63, "圆门": 70, "杨浚": 34, "三棱": 1725, "高潮线": 49, "草里": 439, "尖山湖": 14, "珊瑚藻": 71, "抛球": 569, "游戏场": 259, "护胸": 219, "出局者": 76, "跑上": 992, "拉远": 589, "桃莉": 160, "索姆斯": 33, "劳达": 271, "葛荟婕": 146, "韩恩": 213, "系绳": 175, "李伟森": 15, "敖幼祥": 178, "王伟忠": 464, "李丞责": 40, "杨淑君": 167, "英菲尼迪": 1904, "史陶德": 23, "周士渊": 28, "世熙": 115, "萧天赞": 25, "碧翠": 298, "李英爱": 533, "李学林": 254, "林家琪": 18, "莲花公司": 53, "台西乡": 230, "三井财阀": 55, "不破哲三": 29, "海德尔": 265, "纱衣": 208, "一二百个": 30, "拉明顿": 24, "刘斯奋": 43, "谢伟俊": 171, "吴汉雄": 20, "陈鉴林": 170, "刘浩": 810, "永濑": 160, "陈玉梅": 115, "光明山": 161, "乔治王岛": 275, "错视": 129, "克莱德曼": 859, "冯坤": 244, "洛马纳": 13, "哈利德": 72, "耶娜": 149, "艾娜": 255, "林宇中": 362, "扎维": 297, "锦织圭": 237, "当尼": 123, "洛雷托": 230, "埃方": 66, "准点": 1247, "周守训": 78, "巴拉诺夫": 155, "卡特迈": 62, "麦金利峰": 38, "薛尔": 118, "杰克生": 20, "自海": 120, "永康乡": 46, "新市乡": 67, "鱼塭": 165, "测候": 31, "良地": 58, "竹篙厝": 100, "甲米": 477, "垦拓": 41, "普设": 48, "新港语": 43, "府城隍": 26, "夏林路": 4, "拆建": 347, "五条港": 160, "西门路": 974, "大年初二": 563, "随带": 445, "小庙": 1228, "大匾": 70, "大彻大悟": 827, "显宦": 80, "蔡智恒": 223, "西川满": 118, "南声社": 16, "聚英社": 3, "国剧社": 38, "陈荣盛": 38, "胜雄": 157, "对不上": 2492, "名声在外": 659, "拿不定": 1199, "总得": 6228, "塞维里诺": 14, "五千家": 14, "搭制": 17, "贷款人": 5722, "低税率": 399, "卡多佐": 184, "美国特勤局": 122, "尽享": 1221, "幽林": 277, "别无二致": 308, "耄耋之年": 242, "东大墩街": 7, "八卦形": 49, "大里溪": 150, "筏子": 427, "柳川": 1050, "麻园": 925, "犁头店": 127, "浊水": 559, "鸭川": 776, "新社乡": 48, "沙鹿镇": 72, "丰原市": 178, "潭子": 946, "大雅乡": 79, "望月亭": 41, "张子源": 66, "南屯区": 471, "文心路": 265, "停车格": 105, "住都处": 20, "绿橘": 54, "汇集点": 61, "巨业": 293, "中港路": 153, "中友": 413, "大鲁阁": 153, "文英馆": 34, "文昌庙": 171, "李扬": 385, "林柏榕": 90, "张温鹰": 88, "胡因梦": 202, "吴宇": 626, "赵士强": 38, "朴子市": 215, "中埔村": 11, "大林镇": 458, "扇状": 244, "梅山乡": 343, "清水溪": 265, "隆恩圳": 32, "树头": 805, "顶六": 49, "林仔": 190, "口埤": 10, "林庄": 750, "布袋镇": 183, "鹿草乡": 117, "嘉义县长": 122, "松茸": 1882, "鹿草": 92, "火龙果": 5005, "薏仁": 3846, "农村型": 13, "店面式": 8, "包席": 28, "突增": 368, "麦皮": 298, "柳杉": 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"柔柔": 1558, "沈烈": 11, "陶师": 27, "螫刺": 39, "责任区": 1480, "更香": 1902, "尼龙袜": 137, "冲调": 1586, "尼龙网": 187, "茶煲": 184, "商业点": 52, "殉葬品": 146, "垂爱": 83, "手艺人": 779, "小萝卜": 645, "初具雏形": 159, "膏剂": 474, "包囊": 393, "皮筏": 150, "甘松香": 31, "腰式": 47, "准新郎": 104, "准岳父": 101, "准新娘": 306, "鉴赏力": 474, "北平市委": 83, "热血青年": 887, "敦睦邦交": 12, "王学明": 62, "组织派": 29, "临委": 70, "郭明秋": 33, "高名凯": 17, "市团委": 42, "邹鲁风": 22, "志成中学": 34, "熊熊大火": 278, "走漏风声": 103, "张兆麟": 6, "弘达": 96, "北沟沿": 20, "西四牌楼": 39, "董毓华": 13, "泽云": 113, "北大历史系": 71, "打钟": 162, "竹杆": 470, "石驸马大街": 20, "珠市口": 495, "骡马市大街": 91, "二百五十多": 25, "铁的事实": 133, "洋车": 148, "李明义": 4, "亡国奴": 378, "县局": 717, "导生": 183, "民先队员": 10, "炮局子": 2, "三三一": 90, "韦毓梅": 3, "文总": 146, "汉时关": 352, "命理师": 488, "嘉义人": 74, "祝福礼": 35, "崎玉县": 39, "掠海": 290, "朴次茅斯港": 18, "升降台": 448, "中性美": 78, "旱冰鞋": 302, "口水歌": 762, "票面价值": 503, "成人式": 161, "始祖马": 40, "五千七百万": 11, "久别重逢": 816, "乳用": 185, "骟": 230, "枣红色": 2032, "老马识途": 209, "收银台": 22797, "小跑": 1632, "甘蔗农": 10, "媒界": 44, "变化球": 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"占款": 481, "水淀": 29, "把拽": 61, "渍痕": 26, "阿索林": 3, "瑜轩": 2, "转缺": 28, "柏策": 2, "巴脚": 3, "李化成": 2, "明星性": 8, "探索进": 12, "杨立红": 3, "谢庆华": 4, "夏颖颖": 3, "梳洗完": 14, "温瑞吉": 2, "魂飞胆破": 7, "张嘉恒": 4, "陈秋吉": 2, "辣椒段": 360, "吸管杯": 253, "十点六": 7, "对讲台": 11, "龙国正": 2, "质价": 120, "红堂堂": 7, "扎劲": 9, "邮花": 15, "关汉良": 2, "载回来": 20, "得意扬扬": 108, "王三和": 3, "朱东华": 2, "办结率": 38, "曲南男": 5, "祸手": 20, "抹芽": 117, "花下去": 36, "拍巴掌": 72, "誌": 581, "二七六二": 2, "赖秀峰": 2, "张世兴": 3, "史永强": 3, "沙哈勒": 2, "窦天祥": 2, "地板价": 62, "寄满": 23, "进一波": 9, "李振吉": 4, "柿椒": 185, "劳力型": 36, "阿亨蒂": 2, "何国模": 2, "气泡儿": 7, "跳桩": 3, "马虎虎": 13, "俏生生": 33, "白笈": 6, "赞富": 2, "过时荣": 3, "九十七点四": 2, "诡秘感": 2, "贝方": 5, "徐荣花": 2, "亏本价": 12, "邓海清": 8, "大便器": 196, "搔搔": 19, "草古道": 2, "身子骨儿": 13, "四舅舅": 3, "陕西博物馆": 37, "张二爷": 12, "投机股": 27, "冯金兰": 2, "开花株": 13, "高生亮": 2, "商商量量": 4, "赵苏成": 2, "沙兹利": 4, "嫩气": 21, "吉林市妇联": 2, "熊争艳": 31, "医肚": 6, "五点三八": 2, "黑转粉": 372, "莹丽": 5, "想方设": 16, "焚掉": 8, "漏排": 31, "白条鸭": 73, "一抓到底": 78, "着急成": 6, "张百惠": 2, "二十八点四": 2, "杨子惠": 3, "报账制": 69, "李子林": 20, "维棉": 52, "吉新之": 2, "呜呜咽咽": 49, "社保所": 381, "秦建业": 10, "天之角": 2, "二三千万元": 3, "审量": 9, "邓金兰": 2, "辩证法观": 4, "韩宝善": 2, "长厂": 10, "六点一五": 2, "北新路桥": 35, "可乐瘾": 11, "陈明书": 2, "恶情": 12, "敏茜": 3, "十万八万": 105, "对脸儿": 6, "八字儿": 6, "乔复生": 2, "仁碧": 6, "孟强华": 2, "马家爵": 25, "熊爱英": 2, "搡倒": 17, "砂鼠": 2, "贲思": 2, "惑然": 7, "拳拳之心": 70, "成堆成堆": 49, "啥味呢": 11, "郑裕文": 3, "文秀媛": 2, "唐婶": 8, "廖桂英": 4, "国拍行": 5, "沈勤": 4, "喜鹅": 2, "驻足下来": 7, "意报": 11, "朱文川": 3, "郑清明": 4, "春修": 4, "吴家庆": 3, "劲头儿": 47, "鸣唱声": 8, "探索法": 24, "余璐": 6, "歪嘴子": 18, "四两半": 15, "螺网": 12, "常丽娟": 2, "梁剑锋": 2, "恶声恶气": 33, "王秀茹": 3, "六三亿": 2, "出沫": 41, "陈宏正": 2, "五千二百多": 5, "蒋秋生": 2, "雅克逊": 2, "顺义体委": 2, "夜蛾子": 2, "林草植被": 45, "晚娘脸": 17, "方明山": 2, "郑煌": 7, "射过去": 36, "陈小手": 12, "郭政新": 6, "呼叱": 10, "苦尽甜来": 49, "文化房": 5, "向萍": 28, "勿忙": 33, "药片子": 4, "李双红": 6, "影迷录": 2, "觅求": 29, "李光棋": 2, "前海开源": 141, "张伟达": 6, "野雀儿": 2, "安置率": 6, "张宝安": 3, "背乡离井": 8, "高新产品": 43, "红薯面": 51, "吓不吓人": 39, "焯好": 1017, "满莎": 4, "徐月美": 2, "突突地": 41, "棉苗": 50, "帕特洛娃": 2, "援助点": 9, "种崽": 2, "潘妙兴": 2, "创汇率": 6, "四十三点三": 2, "热儿": 9, "消防痴": 2, "张荣伟": 7, "历海": 6, "郭志超": 2, "加晚": 18, "活题": 11, "陈康雄": 2, "左文龙": 2, "行政装": 6, "塞尔江": 5, "噼噼噗噗": 2, "丁建明": 4, "梁平波": 3, "季均线": 16, "坐寨": 5, "肖明辉": 2, "凯萨琳赫本": 2, "蜜甜蜜甜": 2, "老不休": 10, "两百四": 6, "魏莲生": 6, "大东楼": 2, "王玉江": 9, "一二七一": 3, "王志雷": 3, "项峰": 18, "坡坡坎坎": 25, "畦畦": 15, "木木然": 8, "场景学": 4, "傣寨": 22, "陈华昌": 2, "泥巴沟": 3, "张福友": 12, "四百十一": 2, "七零八碎": 88, "这阵儿": 11, "运行官": 10, "陈桂琴": 2, "黑胫病": 30, "多幺": 16, "花梆步": 2, "孤孤独独": 3, "不对味儿": 27, "大华园": 4, "清幽幽": 13, "捕蚊器": 14, "乳车": 2, "盛大林": 19, "怒海惊涛": 3, "短短长长": 25, "二中胡": 9, "谢长宏": 2, "越级访": 19, "敏姑": 3, "吃来吃去": 88, "破蔽": 5, "撒下去": 54, "王宝乾": 2, "肥头肥脑": 6, "购房户": 319, "马连福": 4, "花繁叶茂": 114, "团伙化": 19, "闹笑": 22, "掸掉": 61, "进城者": 3, "一一一七": 3, "隐逝": 3, "尺谱": 4, "黄检": 11, "涨幅榜": 126, "陈天泰": 2, "任志国": 7, "冠径": 60, "泥土气": 22, "吃饭费": 7, "马小辫": 5, "童俊杰": 2, "阿拉杜": 5, "高文慧": 2, "罗新民": 2, "扎堆儿": 150, "來": 22762, "這個": 7677, 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"瞧着办": 5, "紧跑": 26, "猛地一看": 42, "嚷叫": 37, "厦大图书馆": 8, "强手如林": 88, "剋": 82, "头昏脑涨": 331, "转桌": 38, "苍桑": 90, "答覆": 179, "沾唇": 47, "匡算": 91, "矇": 73, "揽头揽颈": 3, "睏": 40, "投机型": 56, "林秀德": 2, "嫩生生": 18, "溜光水滑": 13, "墟边": 4, "争得过": 36, "扎完": 175, "抿起": 31, "蟪": 142, "拎回": 47, "直掉": 111, "凄栗": 3, "濕": 298, "踉跄跄": 5, "捐完": 48, "献血人": 19, "於是": 1711, "凑上去": 454, "歌詞": 392, "吧哒": 14, "心疱": 3, "忘神": 16, "一股劲儿": 73, "下屬": 31, "杏仁乳": 14, "裉": 187, "老俩口": 112, "文杰武": 2, "糟塌": 35, "抽屜": 16, "显显灵": 14, "演出证": 9, "可人疼": 13, "后面儿": 27, "姦": 208, "粵": 1069, "孔老夫子": 264, "捞完": 49, "联系簿": 23, "钱志新": 6, "熟普": 198, "瞪圆": 84, "笔油": 85, "由衷之言": 11, "玉米糕": 49, "装聋做哑": 12, "郭永志": 2, "簾": 70, "瘪瘪": 161, "胖呼呼": 39, "酒仪": 72, "捣捣": 35, "帽子铺": 5, "胡惧": 3, "紧抿": 64, "牙膏沫": 37, "五三年": 33, "竣工量": 13, "凄历": 9, "捂紧": 198, "囤子": 11, "妙呜": 9, "日後": 184, "富有国": 7, "胃泰": 78, "滋养霜": 91, "目眩神摇": 5, "绷成": 11, "安可声": 7, "九百天": 4, "泛中东": 5, "犬儒式": 8, "固执型": 9, "拣起来": 138, "高世辉": 3, "直溜": 38, "景韵": 15, "危害面": 18, "张士勇": 3, "开秤": 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-0.08407869 0.42554006 0.06170432 0.37133986 0.24146941 0.48000297 0.1742607 0.64067376 0.2745424 0.3705241 0.19021012 -0.059618708 0.17246558 0.38627142 0.07031616 +ဴ -0.6664535 -0.80975753 -0.021253316 0.7081282 0.6487509 0.40911147 1.010123 -0.15629348 1.1478524 -0.28644907 0.18078607 0.03999587 -0.06557093 0.28462884 0.13132353 0.1979602 diff --git a/macropodus/data/model/__init__.py b/macropodus/data/model/__init__.py new file mode 100644 index 0000000..07a10f4 --- /dev/null +++ b/macropodus/data/model/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:46 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/data/words_common/__init__.py b/macropodus/data/words_common/__init__.py new file mode 100644 index 0000000..8fc82c9 --- /dev/null +++ b/macropodus/data/words_common/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 20:29 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/data/words_common/stop_words.py b/macropodus/data/words_common/stop_words.py new file mode 100644 index 0000000..81f91e0 --- /dev/null +++ b/macropodus/data/words_common/stop_words.py @@ -0,0 +1,125 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/26 22:02 +# @author :Mo +# @function :stop_words of dict + +stop_words = {0: "…………………………………………………③", 1: "...................", 2: "......", 3: "关于具体地说", 4: "ＺＸＦＩＴＬ", 5: "）÷（１－", + 6: "－［＊］－", 7: "］∧′＝［", 8: "~~~~", 9: "与此同时", 10: "具体地说", 11: "具体说来", 12: "反过来说", 13: "另一方面", 14: "如上所述", + 15: "尽管如此", 16: "总的来看", 17: "总的来说", 18: "总的说来", 19: "总而言之", 20: "恰恰相反", 21: "换句话说", 22: "由此可见", + 23: "相对而言", 24: "综上所述", 25: "这么点儿", 26: "这就是说", 27: "除此之外", 28: "２．３％", 29: "Ｒ．Ｌ．", 30: "［①①］", + 31: "［①②］", 32: "［①③］", 33: "［①④］", 34: "［①⑤］", 35: "［①⑥］", 36: "［①⑦］", 37: "［①⑧］", 38: "［①⑨］", + 39: "［①Ａ］", 40: "［①Ｂ］", 41: "［①Ｃ］", 42: "［①Ｄ］", 43: "［①Ｅ］", 44: "［①ａ］", 45: "［①ｃ］", 46: "［①ｄ］", + 47: "［①ｅ］", 48: "［①ｆ］", 49: "［①ｇ］", 50: "［①ｈ］", 51: "［①ｉ］", 52: "［①ｏ］", 53: "［②①］", 54: "［②②］", + 55: "［②③］", 56: "［②⑤］", 57: "［②⑥］", 58: "［②⑦］", 59: "［②⑧］", 60: "［②⑩］", 61: "［②Ｂ］", 62: "［②Ｇ］", + 63: "［②ａ］", 64: "［②ｂ］", 65: "［②ｃ］", 66: "［②ｄ］", 67: "［②ｅ］", 68: "［②ｆ］", 69: "［②ｇ］", 70: "［②ｈ］", + 71: "［②ｉ］", 72: "［②ｊ］", 73: "［③①］", 74: "［③⑩］", 75: "［③Ｆ］", 76: "［③ａ］", 77: "［③ｂ］", 78: "［③ｃ］", + 79: "［③ｄ］", 80: "［③ｅ］", 81: "［③ｇ］", 82: "［③ｈ］", 83: "［④ａ］", 84: "［④ｂ］", 85: "［④ｃ］", 86: "［④ｄ］", + 87: "［④ｅ］", 88: "［⑤］］", 89: "［⑤ａ］", 90: "［⑤ｂ］", 91: "［⑤ｄ］", 92: "［⑤ｅ］", 93: "［⑤ｆ］", 94: "...", 95: "://", + 96: "Lex", 97: "exp", 98: "sub", 99: "sup", 100: "×××", 101: "———", 102: "∪φ∈", 103: "》），", 104: "一方面", + 105: "一转眼", 106: "不外乎", 107: "不尽然", 108: "不至于", 109: "与其说", 110: "且不说", 111: "为什么", 112: "乃至于", + 113: "之所以", 114: "于是乎", 115: "什么样", 116: "他们们", 117: "加当期", 118: "中列明", 119: "以至于", 120: "借傥然", + 121: "先不先", 122: "再其次", 123: "再者说", 124: "反过来", 125: "就是了", 126: "就是说", 127: "怎么办", 128: "怎么样", + 129: "换言之", 130: "没奈何", 131: "甚至于", 132: "简言之", 133: "紧接着", 134: "自个儿", 135: "自各儿", 136: "莫不然", + 137: "要不是", 138: "要不然", 139: "这一来", 140: "这么些", 141: "这么样", 142: "这会儿", 143: "那么些", 144: "那么样", + 145: "那会儿", 146: "难道说", 147: "０：２", 148: "１２％", 149: "５：０", 150: "［①］", 151: "［②④", 152: "［②］", + 153: "［③］", 154: "［④］", 155: "［⑤］", 156: "［⑥］", 157: "［⑦］", 158: "［⑧］", 159: "［⑨］", 160: "［⑩］", + 161: "［＊］", 162: "ｎｇ昉", 163: "--", 164: "..", 165: "./", 166: ".一", 167: ".数", 168: ".日", 169: "//", + 170: "::", 171: ">>", 172: "φ．", 173: "——", 174: "’‘", 175: "”，", 176: "……", 177: "′∈", 178: "′｜", + 179: "∈［", 180: "②ｃ", 181: "③］", 182: "──", 183: "〕〔", 184: "一.", 185: "一些", 186: "一何", 187: "一切", + 188: "一则", 189: "一旦", 190: "一来", 191: "一样", 192: "一般", 193: "万一", 194: "上下", 195: "不仅", 196: "不但", + 197: "不光", 198: "不单", 199: "不只", 200: "不如", 201: "不妨", 202: "不尽", 203: "不得", 204: "不怕", 205: "不惟", + 206: "不成", 207: "不拘", 208: "不料", 209: "不是", 210: "不比", 211: "不然", 212: "不特", 213: "不独", 214: "不管", + 215: "不若", 216: "不论", 217: "不过", 218: "不问", 219: "与其", 220: "与否", 221: "且说", 222: "两者", 223: "个别", + 224: "为了", 225: "为何", 226: "为止", 227: "为此", 228: "为着", 229: "乃至", 230: "之一", 231: "之类", 232: "乌乎", + 233: "也好", 234: "也罢", 235: "二来", 236: "于是", 237: "云云", 238: "云尔", 239: "人们", 240: "人家", 241: "什么", + 242: "介于", 243: "仍旧", 244: "从此", 245: "从而", 246: "他人", 247: "他们", 248: "以上", 249: "以为", 250: "以便", + 251: "以免", 252: "以及", 253: "以故", 254: "以期", 255: "以来", 256: "以至", 257: "以致", 258: "任何", 259: "任凭", + 260: "似的", 261: "但凡", 262: "但是", 263: "何以", 264: "何况", 265: "何处", 266: "何时", 267: "余外", 268: "作为", + 269: "你们", 270: "使得", 271: "例如", 272: "依据", 273: "依照", 274: "便于", 275: "俺们", 276: "倘使", 277: "倘或", + 278: "倘然", 279: "倘若", 280: "假使", 281: "假如", 282: "假若", 283: "光是", 284: "全体", 285: "全部", 286: "关于", + 287: "其一", 288: "其中", 289: "其二", 290: "其他", 291: "其余", 292: "其它", 293: "其次", 294: "兼之", 295: "再则", + 296: "再有", 297: "再者", 298: "再说", 299: "况且", 300: "几时", 301: "凡是", 302: "凭借", 303: "出于", 304: "出来", + 305: "分别", 306: "则甚", 307: "别人", 308: "别处", 309: "别是", 310: "别的", 311: "别管", 312: "别说", 313: "前后", + 314: "前此", 315: "前者", 316: "加之", 317: "加以", 318: "即令", 319: "即使", 320: "即便", 321: "即如", 322: "即或", + 323: "即若", 324: "又及", 325: "及其", 326: "及至", 327: "反之", 328: "反而", 329: "受到", 330: "另外", 331: "另悉", + 332: "只当", 333: "只怕", 334: "只是", 335: "只有", 336: "只消", 337: "只要", 338: "只限", 339: "叮咚", 340: "可以", + 341: "可是", 342: "可见", 343: "各个", 344: "各位", 345: "各种", 346: "各自", 347: "同时", 348: "后者", 349: "向使", + 350: "向着", 351: "否则", 352: "吧哒", 353: "呜呼", 354: "呵呵", 355: "呼哧", 356: "咱们", 357: "哈哈", 358: "哎呀", + 359: "哎哟", 360: "哪个", 361: "哪些", 362: "哪儿", 363: "哪天", 364: "哪年", 365: "哪怕", 366: "哪样", 367: "哪边", + 368: "哪里", 369: "哼唷", 370: "唯有", 371: "啪达", 372: "啷当", 373: "喔唷", 374: "嗡嗡", 375: "嘎登", 376: "嘿嘿", + 377: "因为", 378: "因了", 379: "因此", 380: "因着", 381: "因而", 382: "固然", 383: "在下", 384: "在于", 385: "基于", + 386: "处在", 387: "多么", 388: "多少", 389: "大家", 390: "她们", 391: "如上", 392: "如下", 393: "如何", 394: "如其", + 395: "如同", 396: "如是", 397: "如果", 398: "如此", 399: "如若", 400: "始而", 401: "孰料", 402: "孰知", 403: "宁可", + 404: "宁愿", 405: "宁肯", 406: "它们", 407: "对于", 408: "对待", 409: "对方", 410: "对比", 411: "尔后", 412: "尔尔", + 413: "尚且", 414: "就是", 415: "就算", 416: "就要", 417: "尽管", 418: "岂但", 419: "已矣", 420: "巴巴", 421: "并且", + 422: "庶乎", 423: "庶几", 424: "开外", 425: "开始", 426: "归齐", 427: "当地", 428: "当然", 429: "当着", 430: "彼时", + 431: "彼此", 432: "得了", 433: "怎么", 434: "怎奈", 435: "怎样", 436: "总之", 437: "惟其", 438: "慢说", 439: "我们", + 440: "或则", 441: "或是", 442: "或曰", 443: "或者", 444: "截至", 445: "所以", 446: "所在", 447: "所幸", 448: "所有", + 449: "才能", 450: "打从", 451: "抑或", 452: "按照", 453: "据此", 454: "接着", 455: "故此", 456: "故而", 457: "旁人", + 458: "无宁", 459: "无论", 460: "既往", 461: "既是", 462: "既然", 463: "时候", 464: "是以", 465: "是的", 466: "替代", + 467: "有些", 468: "有关", 469: "有及", 470: "有时", 471: "有的", 472: "朝着", 473: "本人", 474: "本地", 475: "本着", + 476: "本身", 477: "来着", 478: "来自", 479: "来说", 480: "极了", 481: "果然", 482: "果真", 483: "某个", 484: "某些", + 485: "某某", 486: "根据", 487: "正值", 488: "正如", 489: "正巧", 490: "正是", 491: "此地", 492: "此处", 493: "此外", + 494: "此时", 495: "此次", 496: "此间", 497: "毋宁", 498: "每当", 499: "比及", 500: "比如", 501: "比方", 502: "沿着", + 503: "漫说", 504: "然则", 505: "然后", 506: "然而", 507: "照着", 508: "犹且", 509: "犹自", 510: "甚且", 511: "甚么", + 512: "甚或", 513: "甚而", 514: "甚至", 515: "用来", 516: "由于", 517: "由是", 518: "由此", 519: "的确", 520: "的话", + 521: "直到", 522: "省得", 523: "眨眼", 524: "着呢", 525: "矣乎", 526: "矣哉", 527: "竟而", 528: "等到", 529: "等等", + 530: "类如", 531: "纵令", 532: "纵使", 533: "纵然", 534: "经过", 535: "结果", 536: "继之", 537: "继后", 538: "继而", + 539: "罢了", 540: "而且", 541: "而况", 542: "而后", 543: "而外", 544: "而已", 545: "而是", 546: "而言", 547: "能否", + 548: "自从", 549: "自后", 550: "自家", 551: "自己", 552: "自打", 553: "自身", 554: "至于", 555: "至今", 556: "至若", + 557: "般的", 558: "若夫", 559: "若是", 560: "若果", 561: "若非", 562: "莫如", 563: "莫若", 564: "虽则", 565: "虽然", + 566: "虽说", 567: "要不", 568: "要么", 569: "要是", 570: "譬喻", 571: "譬如", 572: "许多", 573: "设使", 574: "设或", + 575: "设若", 576: "诚如", 577: "诚然", 578: "说来", 579: "诸位", 580: "诸如", 581: "谁人", 582: "谁料", 583: "谁知", + 584: "贼死", 585: "赖以", 586: "起见", 587: "趁着", 588: "越是", 589: "较之", 590: "还是", 591: "还有", 592: "还要", + 593: "这个", 594: "这么", 595: "这些", 596: "这儿", 597: "这时", 598: "这样", 599: "这次", 600: "这般", 601: "这边", + 602: "这里", 603: "进而", 604: "连同", 605: "逐步", 606: "通过", 607: "遵循", 608: "遵照", 609: "那个", 610: "那么", + 611: "那些", 612: "那儿", 613: "那时", 614: "那样", 615: "那般", 616: "那边", 617: "那里", 618: "鄙人", 619: "鉴于", + 620: "针对", 621: "除了", 622: "除外", 623: "除开", 624: "除非", 625: "随后", 626: "随时", 627: "随着", 628: "非但", + 629: "非徒", 630: "非特", 631: "非独", 632: "顺着", 633: "首先", 634: "）、", 635: "＋ξ", 636: "＋＋", 637: "，也", + 638: "－β", 639: "－－", 640: "１．", 641: "＜±", 642: "＜Δ", 643: "＜λ", 644: "＜φ", 645: "＜＜", 646: "＝″", + 647: "＝☆", 648: "＝（", 649: "＝－", 650: "＝［", 651: "＝｛", 652: "＞λ", 653: "ＬＩ", 654: "［②", 655: "［－", + 656: "［］", 657: "］［", 658: "ａ］", 659: "ｂ］", 660: "ｃ］", 661: "ｅ］", 662: "ｆ］", 663: "｛－", 664: "｝＞", + 665: "～±", 666: "～＋", 667: """, 668: "#", 669: "$", 670: "%", 671: "&", 672: """, 673: "(", 674: ")", + 675: "*", 676: "+", 677: ",", 678: "-", 679: ".", 680: "/", 681: "0", 682: "1", 683: "2", 684: "3", + 685: "4", 686: "5", 687: "6", 688: "7", 689: "8", 690: "9", 691: ":", 692: ";", 693: "<", 694: "=", + 695: ">", 696: "?", 697: "@", 698: "A", 699: "[", 700: "\\", 701: "]", 702: "^", 703: "_", 704: "`", + 705: "|", 706: "}", 707: "~", 708: "·", 709: "×", 710: "Δ", 711: "Ψ", 712: "γ", 713: "μ", 714: "φ", + 715: "В", 716: "—", 717: "‘", 718: "’", 719: "“", 720: "”", 721: "…", 722: "℃", 723: "Ⅲ", 724: "↑", + 725: "→", 726: "≈", 727: "①", 728: "②", 729: "③", 730: "④", 731: "⑤", 732: "⑥", 733: "⑦", 734: "⑧", + 735: "⑨", 736: "⑩", 737: "■", 738: "▲", 739: "、", 740: "。", 741: "〈", 742: "〉", 743: "《", 744: "》", + 745: "」", 746: "『", 747: "』", 748: "【", 749: "】", 750: "〔", 751: "〕", 752: "㈧", 753: "一", 754: "、", + 755: "。", 756: "〈", 757: "〉", 758: "《", 759: "》", 760: "一", 761: "七", 762: "三", 763: "上", 764: "下", + 765: "不", 766: "与", 767: "且", 768: "个", 769: "中", 770: "临", 771: "为", 772: "乃", 773: "么", 774: "之", + 775: "乎", 776: "乘", 777: "九", 778: "也", 779: "了", 780: "二", 781: "于", 782: "五", 783: "些", 784: "亦", + 785: "人", 786: "什", 787: "今", 788: "仍", 789: "从", 790: "他", 791: "以", 792: "们", 793: "任", 794: "会", + 795: "但", 796: "何", 797: "你", 798: "使", 799: "依", 800: "俺", 801: "倘", 802: "借", 803: "做", 804: "像", + 805: "儿", 806: "八", 807: "六", 808: "兮", 809: "共", 810: "其", 811: "内", 812: "再", 813: "冒", 814: "冲", + 815: "几", 816: "凡", 817: "凭", 818: "分", 819: "则", 820: "别", 821: "到", 822: "即", 823: "却", 824: "去", + 825: "又", 826: "及", 827: "另", 828: "只", 829: "叫", 830: "可", 831: "各", 832: "同", 833: "后", 834: "向", + 835: "吓", 836: "吗", 837: "吧", 838: "含", 839: "吱", 840: "呀", 841: "呃", 842: "呕", 843: "呗", 844: "呜", + 845: "呢", 846: "呵", 847: "呸", 848: "咋", 849: "和", 850: "咚", 851: "咦", 852: "咧", 853: "咱", 854: "咳", + 855: "哇", 856: "哈", 857: "哉", 858: "哎", 859: "哗", 860: "哟", 861: "哦", 862: "哩", 863: "哪", 864: "哼", + 865: "唉", 866: "啊", 867: "啐", 868: "啥", 869: "啦", 870: "喂", 871: "喏", 872: "喽", 873: "嗡", 874: "嗬", + 875: "嗯", 876: "嗳", 877: "嘎", 878: "嘘", 879: "嘛", 880: "嘻", 881: "嘿", 882: "四", 883: "因", 884: "在", + 885: "地", 886: "多", 887: "大", 888: "她", 889: "好", 890: "如", 891: "宁", 892: "它", 893: "对", 894: "将", + 895: "小", 896: "尔", 897: "就", 898: "尽", 899: "己", 900: "已", 901: "巴", 902: "年", 903: "并", 904: "归", + 905: "当", 906: "彼", 907: "往", 908: "待", 909: "很", 910: "得", 911: "怎", 912: "您", 913: "我", 914: "或", + 915: "所", 916: "才", 917: "打", 918: "把", 919: "拿", 920: "按", 921: "据", 922: "故", 923: "无", 924: "既", + 925: "日", 926: "时", 927: "是", 928: "更", 929: "曾", 930: "替", 931: "最", 932: "月", 933: "有", 934: "望", + 935: "朝", 936: "本", 937: "来", 938: "某", 939: "欤", 940: "此", 941: "每", 942: "比", 943: "沿", 944: "焉", + 945: "照", 946: "用", 947: "由", 948: "的", 949: "看", 950: "着", 951: "矣", 952: "离", 953: "秒", 954: "第", + 955: "等", 956: "管", 957: "纵", 958: "经", 959: "给", 960: "者", 961: "而", 962: "能", 963: "腾", 964: "自", + 965: "至", 966: "致", 967: "若", 968: "虽", 969: "被", 970: "要", 971: "让", 972: "论", 973: "该", 974: "说", + 975: "请", 976: "诸", 977: "谁", 978: "赶", 979: "起", 980: "趁", 981: "距", 982: "跟", 983: "较", 984: "边", + 985: "过", 986: "还", 987: "这", 988: "连", 989: "那", 990: "都", 991: "阿", 992: "除", 993: "随", 994: "零", + 995: "非", 996: "靠", 997: "顺", 998: "︿", 999: "！", 1000: "＃", 1001: "＄", 1002: "％", 1003: "＆", 1004: "（", + 1005: "）", 1006: "＊", 1007: "＋", 1008: "，", 1009: "０", 1010: "１", 1011: "２", 1012: "３", 1013: "４", + 1014: "５", 1015: "６", 1016: "７", 1017: "８", 1018: "９", 1019: "：", 1020: "；", 1021: "＜", 1022: "＞", + 1023: "？", 1024: "＠", 1025: "［", 1026: "］", 1027: "｛", 1028: "｜", 1029: "｝", 1030: "～", 1031: "￥", + 1032: "︿", 1033: "！", 1034: "＃", 1035: "＄", 1036: "％", 1037: "＆", 1038: "＇", 1039: "（", 1040: "）", + 1041: "＊", 1042: "＋", 1043: "，", 1044: "－", 1045: "．", 1046: "／", 1047: "０", 1048: "１", 1049: "２", + 1050: "３", 1051: "４", 1052: "５", 1053: "６", 1054: "７", 1055: "８", 1056: "９", 1057: "：", 1058: "；", + 1059: "＜", 1060: "＝", 1061: "＞", 1062: "？", 1063: "＠", 1064: "Ａ", 1065: "［", 1066: "］", 1067: "＿", + 1068: "｛", 1069: "｜", 1070: "｝", 1071: "～", 1072: "￥", 1073: "\n", 1074: "", 1075: ",", 1076: " ", + 1077: ";", 1078: "!", 1079: "?", 1080: ". ", 1081: "'", 1082: "\"", 1083: "，"} diff --git a/macropodus/logs/__init__.py b/macropodus/logs/__init__.py new file mode 100644 index 0000000..585cf56 --- /dev/null +++ b/macropodus/logs/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 0:20 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/__init__.py b/macropodus/network/__init__.py new file mode 100644 index 0000000..b1e0893 --- /dev/null +++ b/macropodus/network/__init__.py @@ -0,0 +1,6 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:26 +# @author : Mo +# @function: + diff --git a/macropodus/network/base/__init__.py b/macropodus/network/base/__init__.py new file mode 100644 index 0000000..c57548e --- /dev/null +++ b/macropodus/network/base/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:32 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/base/embedding.py b/macropodus/network/base/embedding.py new file mode 100644 index 0000000..e944a77 --- /dev/null +++ b/macropodus/network/base/embedding.py @@ -0,0 +1,331 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/3 21:29 +# @author :Mo +# @function :embeddings of model, base embedding of random, word2vec or bert + + +from macropodus.preprocess.tools_common import load_json, save_json, txt_read, txt_write +from macropodus.conf.path_config import path_embedding_bert, path_embedding_albert +from macropodus.network.layers.non_mask_layer import NonMaskingLayer +from macropodus.conf.path_config import path_embedding_word2vec_char +from macropodus.conf.path_config import path_embedding_random_char +from macropodus.preprocess.tools_ml import extract_chinese +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.conf.path_config import path_model_dir +from macropodus.conf.path_log import get_logger_root +from gensim.models import KeyedVectors +import tensorflow as tf +import numpy as np +import codecs +import os + + +logger = get_logger_root() + + +class BaseEmbedding: + def __init__(self, hyper_parameters): + self.len_max = hyper_parameters.get('len_max', 50) # 文本最大长度, 建议25-50 + self.embed_size = hyper_parameters.get('embed_size', 300) # 嵌入层尺寸 + self.vocab_size = hyper_parameters.get('vocab_size', 30000) # 字典大小, 这里随便填的，会根据代码里修改 + self.trainable = hyper_parameters.get('trainable', False) # 是否微调, 例如静态词向量、动态词向量、微调bert层等, random也可以 + self.level_type = hyper_parameters.get('level_type', 'char') # 还可以填'word' + self.embedding_type = hyper_parameters.get('embedding_type', 'word2vec') # 词嵌入方式，可以选择'xlnet'、'bert'、'random'、'word2vec' + self.path_model_dir = hyper_parameters.get('model', {}).get("path_model_dir", path_model_dir) # 模型目录, 提供给字/词典 + + # 自适应, 根据level_type和embedding_type判断corpus_path + if self.level_type == "word": + if self.embedding_type == "bert": + raise RuntimeError("bert level_type is 'char', not 'word'") + elif self.embedding_type == "xlnet": + raise RuntimeError("xlnet level_type is 'char', not 'word'") + elif self.embedding_type == "albert": + raise RuntimeError("albert level_type is 'char', not 'word'") + else: + raise RuntimeError("embedding_type must be 'random', 'word2vec' or 'bert'") + elif self.level_type == "char": + if self.embedding_type == "random": + self.corpus_path = hyper_parameters['embedding'].get('corpus_path', path_embedding_random_char) + elif self.embedding_type == "word2vec": + self.corpus_path = hyper_parameters['embedding'].get('corpus_path', path_embedding_word2vec_char) + elif self.embedding_type == "bert": + self.corpus_path = hyper_parameters['embedding'].get('corpus_path', path_embedding_bert) + elif self.embedding_type == "albert": + self.corpus_path = hyper_parameters['embedding'].get('corpus_path', path_embedding_albert) + else: + raise RuntimeError("embedding_type must be 'random', 'word2vec' or 'bert'") + else: + raise RuntimeError("level_type must be 'char' or 'word'") + # 定义的符号 + self.ot_dict = {'[PAD]': 0, + '[UNK]': 1, + '[BOS]': 2, + '[EOS]': 3, } + self.deal_corpus() + self.build() + + def deal_corpus(self): # 处理语料 + pass + + def build(self): + self.token2idx = {} + self.idx2token = {} + + def sentence2idx(self, text): + text = extract_chinese(str(text).upper()) + if self.level_type == 'char': + text = list(text) + elif self.level_type == 'word': + text = macropodus_cut(text) + else: + raise RuntimeError("your input level_type is wrong, it must be 'word' or 'char'") + text = [text_one for text_one in text] + len_leave = self.len_max - len(text) + if len_leave >= 0: + text_index = [self.token2idx[text_char] if text_char in self.token2idx else self.token2idx['[UNK]'] for + text_char in text] + [self.token2idx['[PAD]'] for i in range(len_leave)] + else: + text_index = [self.token2idx[text_char] if text_char in self.token2idx else self.token2idx['[UNK]'] for + text_char in text[0:self.len_max]] + input_mask = min(len(text), self.len_max) + return text_index, input_mask + + def idx2sentence(self, idx): + assert type(idx) == list + text_idx = [self.idx2token[id] if id in self.idx2token else self.idx2token['[UNK]'] for id in idx] + return "".join(text_idx) + + +class RandomEmbedding(BaseEmbedding): + def __init__(self, hyper_parameters): + super().__init__(hyper_parameters) + # self.path = hyper_parameters.get('corpus_path', path_embedding_random_char) + + def deal_corpus(self): + token2idx = self.ot_dict.copy() + count = 3 + if 'term' in self.corpus_path: + with open(file=self.corpus_path, mode='r', encoding='utf-8') as fd: + while True: + term_one = fd.readline() + if not term_one: + break + term_one = term_one.strip() + if term_one not in token2idx: + count = count + 1 + token2idx[term_one] = count + + elif 'corpus' in self.corpus_path: + with open(file=self.corpus_path, mode='r', encoding='utf-8') as fd: + terms = fd.readlines() + for term_one in terms: + if self.level_type == 'char': + text = list(term_one.replace(' ', '').strip()) + elif self.level_type == 'word': + text = macropodus_cut(term_one) + else: + raise RuntimeError("your input level_type is wrong, it must be 'word' or 'char'") + for text_one in text: + if term_one not in token2idx: + count = count + 1 + token2idx[text_one] = count + else: + raise RuntimeError("your input corpus_path is wrong, it must be 'dict' or 'corpus'") + self.token2idx = token2idx + self.idx2token = {} + for key, value in self.token2idx.items(): + self.idx2token[value] = key + + def build(self, **kwargs): + self.vocab_size = len(self.token2idx) + self.input = tf.keras.layers.Input(shape=(self.len_max,), dtype='int32') + self.output = tf.keras.layers.Embedding(self.vocab_size, + self.embed_size, + input_length=self.len_max, + trainable=self.trainable, + )(self.input) + self.model = tf.keras.Model(self.input, self.output) + save_json(json_lines=self.token2idx, json_path=os.path.join(self.path_model_dir, 'vocab.txt')) + + +class WordEmbedding(BaseEmbedding): + def __init__(self, hyper_parameters): + super().__init__(hyper_parameters) + # self.path = hyper_parameters.get('corpus_path', path_embedding_vector_word2vec) + + def build(self, **kwargs): + self.embedding_type = 'word2vec' + # logger.info("load word2vec start!") + self.key_vector = KeyedVectors.load_word2vec_format(self.corpus_path, **kwargs) + # logger.info("load word2vec end!") + self.embed_size = self.key_vector.vector_size + + self.token2idx = self.ot_dict.copy() + embedding_matrix = [] + # 首先加self.token2idx中的四个[PAD]、[UNK]、[BOS]、[EOS] + embedding_matrix.append(np.zeros(self.embed_size)) + embedding_matrix.append(np.random.uniform(-0.5, 0.5, self.embed_size)) + embedding_matrix.append(np.random.uniform(-0.5, 0.5, self.embed_size)) + embedding_matrix.append(np.random.uniform(-0.5, 0.5, self.embed_size)) + + for word in self.key_vector.index2entity: + self.token2idx[word] = len(self.token2idx) + embedding_matrix.append(self.key_vector[word]) + + # self.token2idx = self.token2idx + self.idx2token = {} + for key, value in self.token2idx.items(): + self.idx2token[value] = key + + self.vocab_size = len(self.token2idx) + embedding_matrix = np.array(embedding_matrix) + # self.input = Input(shape=(self.len_max,), dtype='int32') + self.input = tf.keras.layers.Input(shape=(self.len_max,), dtype='int32') + + self.output = tf.keras.layers.Embedding(self.vocab_size, + self.embed_size, + input_length=self.len_max, + weights=[embedding_matrix], + trainable=self.trainable)(self.input) + self.model = tf.keras.Model(self.input, self.output) + # 保存字/词典 + save_json(json_lines=self.token2idx, json_path=os.path.join(self.path_model_dir, 'vocab.txt')) + + +class BertEmbedding(BaseEmbedding): + def __init__(self, hyper_parameters): + self.layer_indexes = hyper_parameters['embedding'].get('layer_indexes', [12]) + super().__init__(hyper_parameters) + + def build(self): + import keras_bert + + self.embedding_type = 'bert' + config_path = os.path.join(self.corpus_path, 'bert_config.json') + check_point_path = os.path.join(self.corpus_path, 'bert_model.ckpt') + dict_path = os.path.join(self.corpus_path, 'vocab.txt') + # logger.info('load bert model start!') + model = keras_bert.load_trained_model_from_checkpoint(config_path, + check_point_path, + seq_len=self.len_max, + trainable=self.trainable) + # logger.info('load bert model success!') + # bert model all layers + layer_dict = [6] + layer_0 = 7 + for i in range(12): + layer_0 = layer_0 + 8 + layer_dict.append(layer_0) + logger.info(layer_dict) + # 输出它本身 + if len(self.layer_indexes) == 0: + encoder_layer = model.output + # 分类如果只有一层，就只取最后那一层的weight；取得不正确，就默认取最后一层 + elif len(self.layer_indexes) == 1: + if self.layer_indexes[0] in [i + 1 for i in range(13)]: + encoder_layer = model.get_layer(index=layer_dict[self.layer_indexes[0] - 1]).output + else: + encoder_layer = model.get_layer(index=layer_dict[-1]).output + # 否则遍历需要取的层，把所有层的weight取出来并拼接起来shape:768*层数 + else: + all_layers = [model.get_layer(index=layer_dict[lay - 1]).output if lay in [i + 1 for i in range(13)] + else model.get_layer(index=layer_dict[-1]).output # 如果给出不正确，就默认输出最后一层 + for lay in self.layer_indexes] + all_layers_select = [] + for all_layers_one in all_layers: + all_layers_select.append(all_layers_one) + encoder_layer = tf.keras.layers.Add()(all_layers_select) + self.output = NonMaskingLayer()(encoder_layer) + self.input = model.inputs + self.model = tf.keras.Model(self.input, self.output) + + self.embedding_size = self.model.output_shape[-1] + + # reader tokenizer + self.token_dict = {} + with codecs.open(dict_path, 'r', 'utf8') as reader: + for line in reader: + token = line.strip() + self.token_dict[token] = len(self.token_dict) + self.vocab_size = len(self.token_dict) + self.tokenizer = keras_bert.Tokenizer(self.token_dict) + + def sentence2idx(self, text, second_text=None): + text = extract_chinese(str(text).upper()) + input_id, input_type_id = self.tokenizer.encode(first=text, second=second_text, max_len=self.len_max) + input_mask = len([1 for ids in input_id if ids == 1]) + return input_id, input_type_id, input_mask + # input_mask = [0 if ids == 0 else 1 for ids in input_id] + # return input_id, input_type_id, input_mask + # return input_id, input_type_id + + +class AlbertEmbedding(BaseEmbedding): + def __init__(self, hyper_parameters): + self.layer_indexes = hyper_parameters['embedding'].get('layer_indexes', [12]) + super().__init__(hyper_parameters) + + def build(self): + from macropodus.network.layers.albert import load_brightmart_albert_zh_checkpoint + import keras_bert + + self.embedding_type = 'albert' + dict_path = os.path.join(self.corpus_path, 'vocab.txt') + # logger.info('load albert model start!') + layer_real = [i for i in range(25)] + [-i for i in range(25)] + # 简要判别一下 + self.layer_indexes = [i if i in layer_real else -2 for i in self.layer_indexes] + self.model = load_brightmart_albert_zh_checkpoint(self.corpus_path, + training=self.trainable, + seq_len=self.len_max, + output_layers = None) # self.layer_indexes) + # model_l = self.model.layers + # logger.info('load albert model success!') + # albert model all layers + layer_dict = [4, 8, 11, 13] + layer_0 = 13 + for i in range(20): + layer_0 = layer_0 + 1 + layer_dict.append(layer_0) + layer_dict.append(34) + logger.info(layer_dict) + # 输出它本身 + if len(self.layer_indexes) == 0: + encoder_layer = self.model.output + # 分类如果只有一层，就只取最后那一层的weight；取得不正确，就默认取最后一层 + elif len(self.layer_indexes) == 1: + if self.layer_indexes[0] in layer_real: + encoder_layer = self.model.get_layer(index=layer_dict[self.layer_indexes[0]]).output + else: + encoder_layer = self.model.get_layer(index=layer_dict[-2]).output + # 否则遍历需要取的层，把所有层的weight取出来并拼接起来shape:768*层数 + else: + all_layers = [self.model.get_layer(index=layer_dict[lay]).output if lay in layer_real + else self.model.get_layer(index=layer_dict[-2]).output # 如果给出不正确，就默认输出最后一层 + for lay in self.layer_indexes] + all_layers_select = [] + for all_layers_one in all_layers: + all_layers_select.append(all_layers_one) + encoder_layer = tf.keras.layers.Add()(all_layers_select) + output = NonMaskingLayer()(encoder_layer) + self.output = [output] + # self.output = [encoder_layer] + self.input = self.model.inputs + self.model = tf.keras.Model(self.input, self.output) + + # reader tokenizer + self.token_dict = {} + with codecs.open(dict_path, 'r', 'utf8') as reader: + for line in reader: + token = line.strip() + self.token_dict[token] = len(self.token_dict) + self.vocab_size = len(self.token_dict) + self.tokenizer = keras_bert.Tokenizer(self.token_dict) + + def sentence2idx(self, text, second_text=None): + text = extract_chinese(str(text).upper()) + input_id, input_type_id = self.tokenizer.encode(first=text, second=second_text, max_len=self.len_max) + input_mask = len([1 for ids in input_id if ids ==1]) + return input_id, input_type_id, input_mask + # return [input_id, input_type_id] diff --git a/macropodus/network/base/graph.py b/macropodus/network/base/graph.py new file mode 100644 index 0000000..4339581 --- /dev/null +++ b/macropodus/network/base/graph.py @@ -0,0 +1,238 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/3 20:51 +# @author :Mo +# @function :graph of base + + +from macropodus.network.preprocess.preprocess_generator import PreprocessGenerator +from macropodus.network.layers.keras_lookahead import Lookahead +from macropodus.preprocess.tools_common import save_json +from macropodus.network.layers.keras_radam import RAdam +from macropodus.conf.path_config import path_model_dir +from macropodus.conf.path_log import get_logger_root +import tensorflow as tf +import numpy as np +import os + + +logger = get_logger_root() + + +class graph: + def __init__(self, hyper_parameters): + """ + 模型初始化 + :param hyper_parameters:json, json["model"] and json["embedding"] + """ + self.len_max = hyper_parameters.get("len_max", 50) # 文本最大长度 + self.embed_size = hyper_parameters.get("embed_size", 300) # 嵌入层尺寸 + self.trainable = hyper_parameters.get("trainable", False) # 是否微调, 例如静态词向量、动态词向量、微调bert层等, random也可以 + self.embedding_type = hyper_parameters.get("embedding_type", "word2vec") # 词嵌入方式，可以选择"xlnet"、"bert"、"gpt-2"、"word2vec"或者"None" + self.gpu_memory_fraction = hyper_parameters.get("gpu_memory_fraction", None) # gpu使用率, 默认不配置 + self.hyper_parameters = hyper_parameters + hyper_parameters_model = hyper_parameters["model"] + self.label = hyper_parameters_model.get("label", 2) # 类型 + self.batch_size = hyper_parameters_model.get("batch_size", 32) # 批向量 + self.filters = hyper_parameters_model.get("filters", [3, 4, 5]) # 卷积核大小 + self.filters_num = hyper_parameters_model.get("filters_num", 300) # 核数 + self.channel_size = hyper_parameters_model.get("channel_size", 1) # 通道数 + self.dropout = hyper_parameters_model.get("dropout", 0.5) # dropout层系数，舍弃 + self.decay_step = hyper_parameters_model.get("decay_step", 100) # 衰减步数 + self.decay_rate = hyper_parameters_model.get("decay_rate", 0.9) # 衰减系数 + self.epochs = hyper_parameters_model.get("epochs", 20) # 训练轮次 + self.vocab_size = hyper_parameters_model.get("vocab_size", 20000) # 字典词典大小 + self.lr = hyper_parameters_model.get("lr", 1e-3) # 学习率 + self.l2 = hyper_parameters_model.get("l2", 1e-6) # l2正则化系数 + self.activate_rnn = hyper_parameters_model.get("activate_rnn", "tanh") # RNN激活函数, tanh, relu, signmod + self.activate_classify = hyper_parameters_model.get("activate_classify", "softmax") # 分类激活函数,softmax或者signmod + self.loss = hyper_parameters_model.get("loss", "categorical_crossentropy") # 损失函数, mse, categorical_crossentropy, sparse_categorical_crossentropy, binary_crossentropy等 + self.metrics = hyper_parameters_model.get("metrics", "accuracy") # acc, binary_accuracy, categorical_accuracy, sparse_categorical_accuracy, sparse_top_k_categorical_accuracy + self.is_training = hyper_parameters_model.get("is_training", False) # 是否训练, 保存时候为Flase,方便预测 + self.patience = hyper_parameters_model.get("patience", 3) # 早停, 2-3就可以了 + self.optimizer_name = hyper_parameters_model.get("optimizer_name", "RAdam,Lookahead") # 早停, 2-3就可以了 + self.path_model_dir = hyper_parameters_model.get("path_model_dir", path_model_dir) # 模型目录 + self.path_fineture = os.path.join(self.path_model_dir, "embedding_trainable.h5") # embedding层保存地址, 例如静态词向量、动态词向量、微调bert层等 + self.path_model = os.path.join(self.path_model_dir, "model.h5") # 模型weight绝对地址 + self.path_hyper_parameters = os.path.join(self.path_model_dir, "params.json") # 超参数保存绝对地址 + self.path_model_l2i_i2l = os.path.join(self.path_model_dir, "l2i_i2l.json") # 模型字典保存绝对地址 + self.path_model_graph = os.path.join(self.path_model_dir, "graph.json") # 模型图结构绝对地址 + if self.gpu_memory_fraction: + # keras, tensorflow控制GPU使用率等 + import tensorflow.python.keras.backend as K + import tensorflow as tf + config = tf.ConfigProto() + config.gpu_options.per_process_gpu_memory_fraction = self.gpu_memory_fraction + # config.gpu_options.allow_growth = True + sess = tf.Session(config=config) + K.set_session(sess) + self.create_model(hyper_parameters) # 模型初始化 + if self.is_training: # 是否是训练阶段, 与预测区分开 + self.create_compile() + self.save_graph() # 保存图结构 + + def create_model(self, hyper_parameters): + """ + 构建神经网络 + :param hyper_parameters: json，超参数 + :return: + """ + # embeddings选择 + if self.embedding_type == "albert": + from macropodus.network.base.embedding import AlbertEmbedding as Embeddings + elif self.embedding_type == "random": + from macropodus.network.base.embedding import RandomEmbedding as Embeddings + elif self.embedding_type == "word2vec": + from macropodus.network.base.embedding import WordEmbedding as Embeddings + elif self.embedding_type == "bert": + from macropodus.network.base.embedding import BertEmbedding as Embeddings + else: + raise RuntimeError("your input embedding_type is wrong, it must be 'random'、 'bert'、 'albert' or 'word2vec'") + # 构建网络层 + self.word_embedding = Embeddings(hyper_parameters=hyper_parameters) + if os.path.exists(self.path_fineture) and self.trainable: + self.word_embedding.model.load_weights(self.path_fineture) + print("load path_fineture ok!") + self.model = None + + def callback(self): + """ + 评价函数、早停 + :return: callback + """ + cb_em = [ + tf.keras.callbacks.EarlyStopping(monitor="val_loss", mode="min", min_delta=1e-8, patience=self.patience), + tf.keras.callbacks.ModelCheckpoint(monitor="val_loss", mode="min", filepath=self.path_model, verbose=1, + save_best_only=True, save_weights_only=False), ] + return cb_em + + def create_compile(self): + """ + 构建优化器、损失函数和评价函数 + :return: + """ + if self.optimizer_name.upper() == "ADAM": + self.model.compile(optimizer=tf.keras.optimizers.Adam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.loss, + metrics=[self.metrics]) # Any optimize + elif self.optimizer_name.upper() == "RADAM": + self.model.compile(optimizer=RAdam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.loss, + metrics=[self.metrics]) # Any optimize + else: + self.model.compile(optimizer=RAdam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.loss, + metrics=[self.metrics]) # Any optimize + lookahead = Lookahead(k=5, alpha=0.5) # Initialize Lookahead + lookahead.inject(self.model) # add into model + + def fit(self, x_train, y_train, x_dev, y_dev): + """ + 训练 + :param x_train: + :param y_train: + :param x_dev: + :param y_dev: + :return: + """ + # 保存超参数 + self.hyper_parameters["model"]["is_training"] = False # 预测时候这些设为False + self.hyper_parameters["model"]["trainable"] = False + self.hyper_parameters["model"]["dropout"] = 1.0 + + save_json(json_lines=self.hyper_parameters, json_path=self.path_hyper_parameters) + # 训练模型 + self.model.fit(x_train, y_train, batch_size=self.batch_size, + epochs=self.epochs, validation_data=(x_dev, y_dev), + shuffle=True, + callbacks=self.callback()) + # 保存embedding, 动态的 + if self.trainable: + self.word_embedding.model.save(self.path_fineture) + + def fit_generator(self, embed, rate=1): + """ + + :param data_fit_generator: yield, 训练数据 + :param data_dev_generator: yield, 验证数据 + :param steps_per_epoch: int, 训练一轮步数 + :param validation_steps: int, 验证一轮步数 + :return: + """ + # 保存超参数 + self.hyper_parameters["model"]["is_training"] = False # 预测时候这些设为False + self.hyper_parameters["model"]["trainable"] = False + self.hyper_parameters["model"]["dropout"] = 1.0 + + save_json(json_lines=self.hyper_parameters, json_path=self.path_hyper_parameters) + + pg = PreprocessGenerator(self.path_model_l2i_i2l) + _, len_train = pg.preprocess_label2set(self.hyper_parameters["data"]["train_data"]) + data_fit_generator = pg.preprocess_label_question_to_idx_fit_generator(embedding_type=self.hyper_parameters["embedding_type"], + batch_size=self.batch_size, + path=self.hyper_parameters["data"]["train_data"], + embed=embed, + rate=rate) + _, len_val = pg.preprocess_label2set(self.hyper_parameters["data"]["val_data"]) + data_dev_generator = pg.preprocess_label_question_to_idx_fit_generator(embedding_type=self.hyper_parameters["embedding_type"], + batch_size=self.batch_size, + path=self.hyper_parameters["data"]["val_data"], + embed=embed, + rate=rate) + steps_per_epoch = len_train // self.batch_size + validation_steps = len_val // self.batch_size + # 训练模型 + self.model.fit_generator(generator=data_fit_generator, + validation_data=data_dev_generator, + callbacks=self.callback(), + epochs=self.epochs, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) + # 保存embedding, 动态的 + if self.trainable: + self.word_embedding.model.save(self.path_fineture) + + def save_graph(self): + """ + 模型图保存 + :return: None + """ + # # 序列化模型embidding + # import pickle + # file_fineture = open(self.path_fineture, "wb") + # pickle.dumps(self.word_embedding.sentence2idx, file_fineture) + # 序列化模型graph + json_string = self.model.to_json() + open(self.path_model_graph, "w", encoding="utf-8").write(json_string) + + def load_model(self): + """ + 模型下载 + :return: None + """ + logger.info("load_model start!") + self.model.load_weights(self.path_model) + logger.info("load_model end!") + + def predict(self, sen): + """ + 预测 + :param sen: + :return: + """ + if self.embedding_type in ["bert", "albert"]: + if type(sen) == np.ndarray: + sen = sen.tolist() + elif type(sen) == list: + sen = sen + else: + raise RuntimeError("your input sen is wrong, it must be type of list or np.array") + return self.model.predict(sen) + else: + if type(sen) == np.ndarray: + sen = sen + elif type(sen) == list: + sen = np.array([sen]) + else: + raise RuntimeError("your input sen is wrong, it must be type of list or np.array") + return self.model.predict(sen) diff --git a/macropodus/network/graph/__init__.py b/macropodus/network/graph/__init__.py new file mode 100644 index 0000000..c57548e --- /dev/null +++ b/macropodus/network/graph/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:32 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/graph/bilstm.py b/macropodus/network/graph/bilstm.py new file mode 100644 index 0000000..57664a3 --- /dev/null +++ b/macropodus/network/graph/bilstm.py @@ -0,0 +1,45 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/19 22:30 +# @author : Mo +# @function: Bi-LSTM + + +from macropodus.network.base.graph import graph +import tensorflow as tf + + +class BiLSTMGraph(graph): + def __init__(self, hyper_parameters): + """ + 初始化 + :param hyper_parameters: json，超参 + """ + self.num_rnn_layers = hyper_parameters['model'].get('num_rnn_layers', 1) + self.rnn_type = hyper_parameters['model'].get('rnn_type', 'LSTM') + self.rnn_units = hyper_parameters['model'].get('rnn_units', 256) + super().__init__(hyper_parameters) + + def create_model(self, hyper_parameters): + """ + 构建神经网络 + :param hyper_parameters:json, hyper parameters of network + :return: tensor, moedl + """ + super().create_model(hyper_parameters) + self.rnn_layer = {'LSTM':tf.keras.layers.LSTM, 'GRU':tf.keras.layers.GRU}[self.rnn_type] + x = self.word_embedding.output + # Bi-LSTM + for nrl in range(self.num_rnn_layers): + x = tf.keras.layers.Bidirectional(self.rnn_layer(units=self.rnn_units, + return_sequences=True, + activation=self.activate_rnn, + kernel_regularizer=tf.keras.regularizers.l2(self.l2), + recurrent_regularizer=tf.keras.regularizers.l2(self.l2) + ))(x) + x = tf.keras.layers.Dropout(self.dropout)(x) + x_time = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(self.label, name='layer_crf_dense'))(x) + x_act = tf.keras.layers.Activation(activation=self.activate_classify)(x_time) + self.output = x_act + self.model = tf.keras.Model(self.word_embedding.input, self.output) + self.model.summary(132) diff --git a/macropodus/network/graph/bilstm_crf.py b/macropodus/network/graph/bilstm_crf.py new file mode 100644 index 0000000..297cbb3 --- /dev/null +++ b/macropodus/network/graph/bilstm_crf.py @@ -0,0 +1,87 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/6 20:45 +# @author : Mo +# @function: Bi-LSTM-CRF + + +from macropodus.network.layers.keras_lookahead import Lookahead +from macropodus.network.layers.keras_radam import RAdam +from macropodus.network.base.graph import graph +from macropodus.network.layers.crf import CRF +import tensorflow as tf + + +class BilstmCRFGraph(graph): + def __init__(self, hyper_parameters): + """ + 初始化 + :param hyper_parameters: json，超参 + """ + self.num_rnn_layers = hyper_parameters['model'].get('num_rnn_layers', 1) # 1, 2, 3 + self.rnn_type = hyper_parameters['model'].get('rnn_type', 'LSTM') # 'LSTM', 'GRU' + self.rnn_units = hyper_parameters['model'].get('rnn_units', 512) # 128, 256, 512, 768, 1024 + self.crf_mode = hyper_parameters['model'].get('crf_mode', 'reg') # "reg", pad + self.supports_masking = hyper_parameters['model'].get('supports_masking', True) # True or False + super().__init__(hyper_parameters) + + def create_model(self, hyper_parameters): + """ + 构建神经网络 + :param hyper_parameters:json, hyper parameters of network + :return: tensor, moedl + """ + super().create_model(hyper_parameters) + # LSTM or GRU + self.rnn_layer = {'LSTM':tf.keras.layers.LSTM, 'GRU':tf.keras.layers.GRU}[self.rnn_type] + x = self.word_embedding.output + # Bi-LSTM + for nrl in range(self.num_rnn_layers): + x = tf.keras.layers.Bidirectional(self.rnn_layer(units=self.rnn_units, + return_sequences=True, + activation=self.activate_rnn, + kernel_regularizer=tf.keras.regularizers.l2(self.l2 * 0.1), + recurrent_regularizer=tf.keras.regularizers.l2(self.l2) + ))(x) + x = tf.keras.layers.Dropout(self.dropout)(x) + # crf, 'pad' or 'reg' + if self.crf_mode == "pad": + # length of real sentence + x_mask = tf.keras.layers.Input(shape=(1), dtype=tf.int32) + self.crf = CRF(self.label, mode='pad', supports_masking=True, name='crf') + tensor = tf.keras.layers.Dense(self.label, name='crf_dense')(x) + self.output = self.crf([tensor, x_mask]) + if self.embedding_type in ["bert", "albert"]: + self.inputs = [self.word_embedding.input[0], self.word_embedding.input[1], x_mask] + else: + self.inputs = [self.word_embedding.input, x_mask] + else: + self.crf = CRF(self.label, mode='reg', name='crf') + tensor = tf.keras.layers.Dense(self.label, name='crf_dense')(x) + self.output = self.crf(tensor) + if self.embedding_type in ["bert", "albert"]: + self.inputs = self.word_embedding.input + else: + self.inputs = self.word_embedding.input + self.model = tf.keras.Model(self.inputs, self.output) + self.model.summary(132) + + def create_compile(self): + """ + 构建优化器、损失函数和评价函数 + :return: + """ + if self.optimizer_name.upper() == "ADAM": + self.model.compile(optimizer=tf.keras.optimizers.Adam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.crf.loss, + metrics=[self.crf.viterbi_accuracy]) # Any optimize, [self.metrics]) + elif self.optimizer_name.upper() == "RADAM": + self.model.compile(optimizer=RAdam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.crf.loss, + metrics=[self.crf.viterbi_accuracy]) # Any optimize + else: + self.model.compile(optimizer=RAdam(lr=self.lr, beta_1=0.9, beta_2=0.999, decay=0.0), + loss=self.crf.loss, + metrics=[self.crf.viterbi_accuracy]) # Any optimize + lookahead = Lookahead(k=5, alpha=0.5) # Initialize Lookahead + lookahead.inject(self.model) # add into model diff --git a/macropodus/network/layers/__init__.py b/macropodus/network/layers/__init__.py new file mode 100644 index 0000000..c8d2854 --- /dev/null +++ b/macropodus/network/layers/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/3 20:43 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/layers/albert.py b/macropodus/network/layers/albert.py new file mode 100644 index 0000000..d99f8a4 --- /dev/null +++ b/macropodus/network/layers/albert.py @@ -0,0 +1,331 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/22 20:35 +# @author :TinkerMob +# @function :keras_albert_model +# @codefrom :https://github.com/TinkerMob/keras_albert_model + + +from keras_adaptive_softmax import AdaptiveEmbedding, AdaptiveSoftmax +from keras_bert import get_custom_objects as get_bert_custom_objects +from keras_position_wise_feed_forward import FeedForward +from keras_layer_normalization import LayerNormalization +from keras_bert.activations.gelu_fallback import gelu +from keras_multi_head import MultiHeadAttention +from keras_bert.layers import Masked, Extract +from keras_pos_embd import PositionEmbedding +from keras_bert.backend import keras +import tensorflow as tf +import numpy as np +import json +import os + + +__all__ = [ + 'get_custom_objects', 'build_albert', + 'load_brightmart_albert_zh_checkpoint', +] + + +def get_custom_objects(): + custom_objects = get_bert_custom_objects() + custom_objects['AdaptiveEmbedding'] = AdaptiveEmbedding + custom_objects['AdaptiveSoftmax'] = AdaptiveSoftmax + return custom_objects + + +def build_albert(token_num, + pos_num=512, + seq_len=512, + embed_dim=128, + hidden_dim=768, + transformer_num=12, + head_num=12, + feed_forward_dim=3072, + dropout_rate=0.1, + attention_activation=None, + feed_forward_activation='gelu', + training=True, + trainable=None, + output_layers=None): + """Get ALBERT model. + See: https://arxiv.org/pdf/1909.11942.pdf + :param token_num: Number of tokens. + :param pos_num: Maximum position. + :param seq_len: Maximum length of the input sequence or None. + :param embed_dim: Dimensions of embeddings. + :param hidden_dim: Dimensions of hidden layers. + :param transformer_num: Number of transformers. + :param head_num: Number of heads in multi-head attention + in each transformer. + :param feed_forward_dim: Dimension of the feed forward layer + in each transformer. + :param dropout_rate: Dropout rate. + :param attention_activation: Activation for attention layers. + :param feed_forward_activation: Activation for feed-forward layers. + :param training: A built model with MLM and NSP outputs will be returned + if it is `True`, otherwise the input layers and the last + feature extraction layer will be returned. + :param trainable: Whether the model is trainable. + :param output_layers: A list of indices of output layers. + """ + if attention_activation == 'gelu': + attention_activation = gelu + if feed_forward_activation == 'gelu': + feed_forward_activation = gelu + if trainable is None: + trainable = training + + def _trainable(_layer): + if isinstance(trainable, (list, tuple, set)): + for prefix in trainable: + if _layer.name.startswith(prefix): + return True + return False + return trainable + + # Build inputs + input_token = keras.layers.Input(shape=(seq_len,), name='Input-Token') + input_segment = keras.layers.Input(shape=(seq_len,), name='Input-Segment') + inputs = [input_token, input_segment] + + # Build embeddings + embed_token, embed_weights, embed_projection = AdaptiveEmbedding( + input_dim=token_num, + output_dim=hidden_dim, + embed_dim=embed_dim, + mask_zero=True, + trainable=trainable, + return_embeddings=True, + return_projections=True, + name='Embed-Token', + )(input_token) + embed_segment = keras.layers.Embedding( + input_dim=2, + output_dim=hidden_dim, + trainable=trainable, + name='Embed-Segment', + )(input_segment) + embed_layer = keras.layers.Add(name='Embed-Token-Segment')( + [embed_token, embed_segment]) + embed_layer = PositionEmbedding( + input_dim=pos_num, + output_dim=hidden_dim, + mode=PositionEmbedding.MODE_ADD, + trainable=trainable, + name='Embedding-Position', + )(embed_layer) + + if dropout_rate > 0.0: + dropout_layer = keras.layers.Dropout( + rate=dropout_rate, + name='Embedding-Dropout', + )(embed_layer) + else: + dropout_layer = embed_layer + embed_layer = LayerNormalization( + trainable=trainable, + name='Embedding-Norm', + )(dropout_layer) + + # Build shared transformer + attention_layer = MultiHeadAttention( + head_num=head_num, + activation=attention_activation, + name='Attention', + ) + attention_normal = LayerNormalization(name='Attention-Normal') + feed_forward_layer = FeedForward( + units=feed_forward_dim, + activation=feed_forward_activation, + name='Feed-Forward' + ) + feed_forward_normal = LayerNormalization(name='Feed-Forward-Normal') + + transformed = embed_layer + transformed_layers = [] + for i in range(transformer_num): + attention_input = transformed + transformed = attention_layer(transformed) + if dropout_rate > 0.0: + transformed = keras.layers.Dropout( + rate=dropout_rate, + name='Attention-Dropout-{}'.format(i + 1), + )(transformed) + transformed = keras.layers.Add( + name='Attention-Add-{}'.format(i + 1), + )([attention_input, transformed]) + transformed = attention_normal(transformed) + + feed_forward_input = transformed + transformed = feed_forward_layer(transformed) + if dropout_rate > 0.0: + transformed = keras.layers.Dropout( + rate=dropout_rate, + name='Feed-Forward-Dropout-{}'.format(i + 1), + )(transformed) + transformed = keras.layers.Add( + name='Feed-Forward-Add-{}'.format(i + 1), + )([feed_forward_input, transformed]) + transformed = feed_forward_normal(transformed) + transformed_layers.append(transformed) + + if training: + # Build tasks + mlm_dense_layer = keras.layers.Dense( + units=hidden_dim, + activation=feed_forward_activation, + name='MLM-Dense', + )(transformed) + mlm_norm_layer = LayerNormalization(name='MLM-Norm')(mlm_dense_layer) + mlm_pred_layer = AdaptiveSoftmax( + input_dim=hidden_dim, + output_dim=token_num, + embed_dim=embed_dim, + bind_embeddings=True, + bind_projections=True, + name='MLM-Sim', + )([mlm_norm_layer, embed_weights, embed_projection]) + masked_layer = Masked(name='MLM')([mlm_pred_layer, inputs[-1]]) + extract_layer = Extract(index=0, name='Extract')(transformed) + nsp_dense_layer = keras.layers.Dense( + units=hidden_dim, + activation='tanh', + name='SOP-Dense', + )(extract_layer) + nsp_pred_layer = keras.layers.Dense( + units=2, + activation='softmax', + name='SOP', + )(nsp_dense_layer) + model = keras.models.Model( + inputs=inputs, + outputs=[masked_layer, nsp_pred_layer]) + for layer in model.layers: + layer.trainable = _trainable(layer) + return model + if output_layers is not None: + if isinstance(output_layers, list): + output_layers = [ + transformed_layers[index] for index in output_layers] + output = keras.layers.Concatenate( + name='Output', + )(output_layers) + else: + output = transformed_layers[output_layers] + model = keras.models.Model(inputs=inputs, outputs=output) + return model + model = keras.models.Model(inputs=inputs, outputs=transformed) + for layer in model.layers: + layer.trainable = _trainable(layer) + return inputs, transformed + + +def load_brightmart_albert_zh_checkpoint(checkpoint_path, **kwargs): + """Load checkpoint from https://github.com/brightmart/albert_zh + :param checkpoint_path: path to checkpoint folder. + :param kwargs: arguments for albert model. + :return: + """ + config = {} + for file_name in os.listdir(checkpoint_path): + if file_name.startswith('bert_config.json'): + with open(os.path.join(checkpoint_path, file_name)) as reader: + config = json.load(reader) + break + + def _set_if_not_existed(key, value): + if key not in kwargs: + kwargs[key] = value + + # 修改部分,必须输入is_training, len_max + training = kwargs['training'] + # config['max_position_embeddings'] = config['max_position_embeddings'] = kwargs['len_max'] + _set_if_not_existed('training', True) + _set_if_not_existed('token_num', config['vocab_size']) + _set_if_not_existed('pos_num', config['max_position_embeddings']) + _set_if_not_existed('seq_len', config['max_position_embeddings']) + _set_if_not_existed('embed_dim', config['embedding_size']) + _set_if_not_existed('hidden_dim', config['hidden_size']) + _set_if_not_existed('transformer_num', config['num_hidden_layers']) + _set_if_not_existed('head_num', config['num_attention_heads']) + _set_if_not_existed('feed_forward_dim', config['intermediate_size']) + _set_if_not_existed('dropout_rate', config['hidden_dropout_prob']) + _set_if_not_existed('feed_forward_activation', config['hidden_act']) + + model = build_albert(**kwargs) + if not training: + inputs, outputs = model + model = keras.models.Model(inputs, outputs) + + def _checkpoint_loader(checkpoint_file): + def _loader(name): + return tf.train.load_variable(checkpoint_file, name) + return _loader + + loader = _checkpoint_loader( + os.path.join(checkpoint_path, 'bert_model.ckpt')) + + model.get_layer(name='Embed-Token').set_weights([ + loader('bert/embeddings/word_embeddings'), + loader('bert/embeddings/word_embeddings_2'), + ]) + model.get_layer(name='Embed-Segment').set_weights([ + loader('bert/embeddings/token_type_embeddings'), + ]) + model.get_layer(name='Embedding-Position').set_weights([ + loader('bert/embeddings/position_embeddings'), + ]) + model.get_layer(name='Embedding-Norm').set_weights([ + loader('bert/embeddings/LayerNorm/gamma'), + loader('bert/embeddings/LayerNorm/beta'), + ]) + + model.get_layer(name='Attention').set_weights([ + loader('bert/encoder/layer_shared/attention/self/query/kernel'), + loader('bert/encoder/layer_shared/attention/self/query/bias'), + loader('bert/encoder/layer_shared/attention/self/key/kernel'), + loader('bert/encoder/layer_shared/attention/self/key/bias'), + loader('bert/encoder/layer_shared/attention/self/value/kernel'), + loader('bert/encoder/layer_shared/attention/self/value/bias'), + loader('bert/encoder/layer_shared/attention/output/dense/kernel'), + loader('bert/encoder/layer_shared/attention/output/dense/bias'), + ]) + model.get_layer(name='Attention-Normal').set_weights([ + loader('bert/encoder/layer_shared/attention/output/LayerNorm/gamma'), + loader('bert/encoder/layer_shared/attention/output/LayerNorm/beta'), + ]) + model.get_layer(name='Feed-Forward').set_weights([ + loader('bert/encoder/layer_shared/intermediate/dense/kernel'), + loader('bert/encoder/layer_shared/intermediate/dense/bias'), + loader('bert/encoder/layer_shared/output/dense/kernel'), + loader('bert/encoder/layer_shared/output/dense/bias'), + ]) + model.get_layer(name='Feed-Forward-Normal').set_weights([ + loader('bert/encoder/layer_shared/output/LayerNorm/gamma'), + loader('bert/encoder/layer_shared/output/LayerNorm/beta'), + ]) + + if training: + model.get_layer(name='MLM-Dense').set_weights([ + loader('cls/predictions/transform/dense/kernel'), + loader('cls/predictions/transform/dense/bias'), + ]) + model.get_layer(name='MLM-Norm').set_weights([ + loader('cls/predictions/transform/LayerNorm/gamma'), + loader('cls/predictions/transform/LayerNorm/beta'), + ]) + model.get_layer(name='MLM-Sim').set_weights([ + loader('cls/predictions/output_bias'), + ]) + + model.get_layer(name='SOP-Dense').set_weights([ + loader('bert/pooler/dense/kernel'), + loader('bert/pooler/dense/bias'), + ]) + model.get_layer(name='SOP').set_weights([ + np.transpose(loader('cls/seq_relationship/output_weights')), + loader('cls/seq_relationship/output_bias'), + ]) + + return model diff --git a/macropodus/network/layers/crf.py b/macropodus/network/layers/crf.py new file mode 100644 index 0000000..a279ae6 --- /dev/null +++ b/macropodus/network/layers/crf.py @@ -0,0 +1,128 @@ +# encoding: utf-8 + +# author: BrikerMan +# contact: eliyar917@gmail.com +# blog: https://eliyar.biz +# code from: + +# file: crf.py +# time: 2019-06-28 14:33 + + +import tensorflow as tf + + +class CRF(tf.keras.layers.Layer): + """ + Conditional Random Field layer (tf.keras) + `CRF` can be used as the last layer in a network (as a classifier). Input shape (features) + must be equal to the number of classes the CRF can predict (a linear layer is recommended). + Note: the loss and accuracy functions of networks using `CRF` must + use the provided loss and accuracy functions (denoted as loss and viterbi_accuracy) + as the classification of sequences are used with the layers internal weights. + Args: + output_dim (int): the number of labels to tag each temporal input. + Input shape: + nD tensor with shape `(batch_size, sentence length, num_classes)`. + Output shape: + nD tensor with shape: `(batch_size, sentence length, num_classes)`. + """ + + def __init__(self, + output_dim, + mode='reg', + supports_masking=False, + transitions=None, + **kwargs): + self.transitions = None + super(CRF, self).__init__(**kwargs) + self.output_dim = int(output_dim) + self.mode = mode + if self.mode == 'pad': + self.input_spec = [tf.keras.layers.InputSpec(min_ndim=3), tf.keras.layers.InputSpec(min_ndim=2)] + elif self.mode == 'reg': + self.input_spec = tf.keras.layers.InputSpec(min_ndim=3) + else: + raise ValueError + self.supports_masking = supports_masking + self.sequence_lengths = None + + def get_config(self): + config = { + 'output_dim': self.output_dim, + 'mode': self.mode, + 'supports_masking': self.supports_masking, + 'transitions': tf.keras.backend.eval(self.transitions) + } + base_config = super(CRF, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + def build(self, input_shape): + if self.mode == 'pad': + assert len(input_shape) == 2 + assert len(input_shape[0]) == 3 + assert len(input_shape[1]) == 2 + f_shape = tf.TensorShape(input_shape[0]) + input_spec = [tf.keras.layers.InputSpec(min_ndim=3, axes={-1: f_shape[-1]}), + tf.keras.layers.InputSpec(min_ndim=2, axes={-1: 1}, dtype=tf.int32)] + else: + assert len(input_shape) == 3 + f_shape = tf.TensorShape(input_shape) + input_spec = tf.keras.layers.InputSpec(min_ndim=3, axes={-1: f_shape[-1]}) + + if f_shape[-1] is None: + raise ValueError('The last dimension of the inputs to `CRF` should be defined. Found `None`.') + if f_shape[-1] != self.output_dim: + raise ValueError('The last dimension of the input shape must be equal to output shape. ' + 'Use a linear layer if needed.') + self.input_spec = input_spec + self.transitions = self.add_weight(name='transitions', + shape=[self.output_dim, self.output_dim], + initializer='glorot_uniform', + trainable=True) + self.built = True + + def call(self, inputs, **kwargs): + if self.mode == 'pad': + sequences = tf.convert_to_tensor(inputs[0], dtype=self.dtype) + self.sequence_lengths = tf.keras.backend.flatten(inputs[-1]) + else: + sequences = tf.convert_to_tensor(inputs, dtype=self.dtype) + shape = tf.shape(inputs) + self.sequence_lengths = tf.ones(shape[0], dtype=tf.int32) * (shape[1]) + viterbi_sequence, _ = tf.contrib.crf.crf_decode(sequences, self.transitions, + self.sequence_lengths) + output = tf.keras.backend.one_hot(viterbi_sequence, self.output_dim) + return tf.keras.backend.in_train_phase(sequences, output) + + def loss(self, y_true, y_pred): + y_pred = tf.convert_to_tensor(y_pred, dtype=self.dtype) + log_likelihood, self.transitions = tf.contrib.crf.crf_log_likelihood(y_pred, + tf.cast(tf.keras.backend.argmax(y_true), + dtype=tf.int32), + self.sequence_lengths, + transition_params=self.transitions) + # loss_crf = tf.reduce_mean(-log_likelihood) + return tf.reduce_mean(-log_likelihood) + # return tf.math.log(loss_crf) + + def compute_output_shape(self, input_shape): + if self.mode == 'pad': + data_shape = input_shape[0] + else: + data_shape = input_shape + tf.TensorShape(data_shape).assert_has_rank(3) + return data_shape[:2] + (self.output_dim,) + + @property + def viterbi_accuracy(self): + def accuracy(y_true, y_pred): + shape = tf.shape(y_pred) + sequence_lengths = tf.ones(shape[0], dtype=tf.int32) * (shape[1]) + viterbi_sequence, _ = tf.contrib.crf.crf_decode(y_pred, self.transitions, sequence_lengths) + output = tf.keras.backend.one_hot(viterbi_sequence, self.output_dim) + return tf.keras.metrics.categorical_accuracy(y_true, output) + + accuracy.func_name = 'viterbi_accuracy' + return accuracy + diff --git a/macropodus/network/layers/keras_lookahead.py b/macropodus/network/layers/keras_lookahead.py new file mode 100644 index 0000000..27f6786 --- /dev/null +++ b/macropodus/network/layers/keras_lookahead.py @@ -0,0 +1,77 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/12 21:14 +# @author : Mo +# @function: lookahead of keras +# @codefrom: https://github.com/bojone/keras_lookahead + + +import tensorflow.python.keras.backend as K + + +class Lookahead(object): + """Add the [Lookahead Optimizer](https://arxiv.org/abs/1907.08610) functionality for [keras](https://keras.io/). + """ + + def __init__(self, k=5, alpha=0.5): + self.k = k + self.alpha = alpha + self.count = 0 + + def inject(self, model): + """Inject the Lookahead algorithm for the given model. + The following code is modified from keras's _make_train_function method. + See: https://github.com/keras-team/keras/blob/master/keras/engine/training.py#L497 + """ + if not hasattr(model, 'train_function'): + raise RuntimeError('You must compile your model before using it.') + + model._check_trainable_weights_consistency() + + if model.train_function is None: + inputs = (model._feed_inputs + + model._feed_targets + + model._feed_sample_weights) + if model._uses_dynamic_learning_phase(): + inputs += [K.learning_phase()] + fast_params = model._collected_trainable_weights + + with K.name_scope('training'): + with K.name_scope(model.optimizer.__class__.__name__): + training_updates = model.optimizer.get_updates( + params=fast_params, + loss=model.total_loss) + slow_params = [K.variable(p) for p in fast_params] + fast_updates = (model.updates + + training_updates + + model.metrics_updates) + + slow_updates, copy_updates = [], [] + for p, q in zip(fast_params, slow_params): + slow_updates.append(K.update(q, q + self.alpha * (p - q))) + copy_updates.append(K.update(p, q)) + + # Gets loss and metrics. Updates weights at each call. + fast_train_function = K.function( + inputs, + [model.total_loss] + model.metrics_tensors, + updates=fast_updates, + name='fast_train_function', + **model._function_kwargs) + + def F(inputs): + self.count += 1 + R = fast_train_function(inputs) + if self.count % self.k == 0: + K.batch_get_value(slow_updates) + K.batch_get_value(copy_updates) + return R + + model.train_function = F + +if __name__ == '__main__': + gg = 0 + # # useage + # model.compile(optimizer=Adam(1e-3), loss='mse') # Any optimizer + # lookahead = Lookahead(k=5, alpha=0.5) # Initialize Lookahead + # lookahead.inject(model) # add into model diff --git a/macropodus/network/layers/keras_radam.py b/macropodus/network/layers/keras_radam.py new file mode 100644 index 0000000..9c5b9f5 --- /dev/null +++ b/macropodus/network/layers/keras_radam.py @@ -0,0 +1,96 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/12 20:12 +# @author : Mo +# @function: radam of keras +# @codefrom: https://github.com/bojone/keras_radam + + +from tensorflow.python.keras.optimizers import Optimizer +# from tensorflow.python.keras.legacy import interfaces +import tensorflow.python.keras.backend as K + + +class RAdam(Optimizer): + """RAdam optimizer. + Default parameters follow those provided in the original Adam paper. + # Arguments + lr: float >= 0. Learning rate. + beta_1: float, 0 < beta < 1. Generally close to 1. + beta_2: float, 0 < beta < 1. Generally close to 1. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. + decay: float >= 0. Learning rate decay over each update. + amsgrad: boolean. Whether to apply the AMSGrad variant of this + algorithm from the paper "On the Convergence of Adam and + Beyond". + # References + - [RAdam - A Method for Stochastic Optimization] + (https://arxiv.org/abs/1908.03265) + - [On The Variance Of The Adaptive Learning Rate And Beyond] + (https://arxiv.org/abs/1908.03265) + """ + + def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, + epsilon=None, decay=0., **kwargs): + super(RAdam, self).__init__(**kwargs) + with K.name_scope(self.__class__.__name__): + self.iterations = K.variable(0, dtype='int64', name='iterations') + self.lr = K.variable(lr, name='lr') + self.beta_1 = K.variable(beta_1, name='beta_1') + self.beta_2 = K.variable(beta_2, name='beta_2') + self.decay = K.variable(decay, name='decay') + if epsilon is None: + epsilon = K.epsilon() + self.epsilon = epsilon + self.initial_decay = decay + + # @interfaces.legacy_get_updates_support + def get_updates(self, loss, params): + grads = self.get_gradients(loss, params) + self.updates = [K.update_add(self.iterations, 1)] + + lr = self.lr + if self.initial_decay > 0: + lr = lr * (1. / (1. + self.decay * K.cast(self.iterations, + K.dtype(self.decay)))) + + t = K.cast(self.iterations, K.floatx()) + 1 + beta_1_t = K.pow(self.beta_1, t) + beta_2_t = K.pow(self.beta_2, t) + rho = 2 / (1 - self.beta_2) - 1 + rho_t = rho - 2 * t * beta_2_t / (1 - beta_2_t) + r_t = K.sqrt( + K.relu(rho_t - 4) * K.relu(rho_t - 2) * rho / ((rho - 4) * (rho - 2) * rho_t) + ) + flag = K.cast(rho_t > 4, K.floatx()) + + ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] + vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] + self.weights = [self.iterations] + ms + vs + + for p, g, m, v in zip(params, grads, ms, vs): + m_t = (self.beta_1 * m) + (1. - self.beta_1) * g + v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g) + mhat_t = m_t / (1 - beta_1_t) + vhat_t = K.sqrt(v_t / (1 - beta_2_t)) + p_t = p - lr * mhat_t * (flag * r_t / (vhat_t + self.epsilon) + (1 - flag)) + + self.updates.append(K.update(m, m_t)) + self.updates.append(K.update(v, v_t)) + new_p = p_t + + # Apply constraints. + if getattr(p, 'constraint', None) is not None: + new_p = p.constraint(new_p) + + self.updates.append(K.update(p, new_p)) + return self.updates + + def get_config(self): + config = {'lr': float(K.get_value(self.lr)), + 'beta_1': float(K.get_value(self.beta_1)), + 'beta_2': float(K.get_value(self.beta_2)), + 'decay': float(K.get_value(self.decay)), + 'epsilon': self.epsilon} + base_config = super(RAdam, self).get_config() + return dict(list(base_config.items()) + list(config.items())) \ No newline at end of file diff --git a/macropodus/network/layers/non_mask_layer.py b/macropodus/network/layers/non_mask_layer.py new file mode 100644 index 0000000..e9eb1dc --- /dev/null +++ b/macropodus/network/layers/non_mask_layer.py @@ -0,0 +1,31 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/10 21:35 +# @author :Mo +# @function :NonMaskingLayer of bert +# @codefrom :https://github.com/jacoxu + + +from __future__ import print_function, division +from tensorflow.python.keras.layers import Layer + + +class NonMaskingLayer(Layer): + """ + fix convolutional 1D can't receive masked input, + detail: https://github.com/keras-team/keras/issues/4978 + """ + + def __init__(self, **kwargs): + self.supports_masking = True + super(NonMaskingLayer, self).__init__(**kwargs) + + def build(self, input_shape): + pass + + def compute_mask(self, inputs, input_mask=None): + # do not pass the mask to the next layers + return None + + def call(self, x, mask=None): + return x diff --git a/macropodus/network/predict/__init__.py b/macropodus/network/predict/__init__.py new file mode 100644 index 0000000..5f4f83a --- /dev/null +++ b/macropodus/network/predict/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 23:08 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/predict/predict_albert_bilstm.py b/macropodus/network/predict/predict_albert_bilstm.py new file mode 100644 index 0000000..f6d8231 --- /dev/null +++ b/macropodus/network/predict/predict_albert_bilstm.py @@ -0,0 +1,81 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 23:08 +# @author : Mo +# @function: predict(albert+bilstm) + + +from macropodus.conf.path_config import path_embedding_albert +from macropodus.preprocess.tools_ml import extract_chinese +from tensorflow.python.keras.models import model_from_json +from macropodus.preprocess.tools_common import load_json +from macropodus.conf.path_config import path_model_dir +from keras_bert import Tokenizer +import numpy as np +import macropodus +import codecs +import pickle +import os + + +path_dir = path_model_dir # + "/ner_albert_bilstm_people_199801" +# 加载模型结构 +model = model_from_json(open(path_dir+"/graph.json", "r", encoding="utf-8").read(), + custom_objects=macropodus.custom_objects) +# 加载模型权重 +model.load_weights(path_dir+"/model.h5") + +# reader tokenizer +token_dict = {} +path_dict = os.path.join(path_embedding_albert, "vocab.txt") +with codecs.open(path_dict, 'r', 'utf8') as reader: + for line in reader: + token = line.strip() + token_dict[token] = len(token_dict) +vocab_size = len(token_dict) +tokenizer = Tokenizer(token_dict) +# params +path_params = path_dir + "/params.json" +params = load_json(path_params) +len_max = params["len_max"] +# l2i_i2l +path_l2i_i2l = path_dir + "/l2i_i2l.json" +l2i_i2l = load_json(path_l2i_i2l) + +def sentence2idx(text, second_text=None): + text = extract_chinese(str(text).upper()) + input_id, input_type_id = tokenizer.encode(first=text, second=second_text, max_len=len_max) + input_mask = len([1 for ids in input_id if ids != 0]) + # return input_id, input_type_id, input_mask + # x_ = np.array((input_id, input_type_id, input_mask)) + x = [[input_id, input_type_id, input_mask]] + x_ = np.array(x) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + x_3 = np.array([x[2] for x in x_]) + + return [x_1, x_2, x_3] + +while True: + print("请输入:") + ques = input() + mode_input = sentence2idx(ques) + res = model.predict(mode_input) + res_list = res.tolist()[0] + res_idxs = [np.argmax(rl) for rl in res_list] + res_label = [l2i_i2l["i2l"][str(ri)] if str(ri) in l2i_i2l["i2l"] else "O" for ri in res_idxs] + print(res_label[:len(ques)]) + +# gg = 0 + +# # 保存模型的结构 +# json_string = model.to_json() # 方式1 +# open("model_architecture_1.json", "w").write(json_string) +# yaml_string = model.to_yaml() # 方式2 +# open("model_arthitecture_2.yaml", "w").write(yaml_string) +# # 加载模型结构 +# model = model_from_json(open("model_architecture_1.json", "r").read()) +# # 加载模型权重 +# model.load_weights("weights-improvement-40-0.96208.hdf5") +# # 编译模型 +# model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER, metrics=["accuracy"]) \ No newline at end of file diff --git a/macropodus/network/predict/predict_w2v_bilstm.py b/macropodus/network/predict/predict_w2v_bilstm.py new file mode 100644 index 0000000..b2f856e --- /dev/null +++ b/macropodus/network/predict/predict_w2v_bilstm.py @@ -0,0 +1,73 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 23:08 +# @author : Mo +# @function: predict(albert+bilstm) + + +from macropodus.conf.path_config import path_embedding_albert +from macropodus.preprocess.tools_ml import extract_chinese +from tensorflow.python.keras.models import model_from_json +from macropodus.preprocess.tools_common import load_json +from macropodus.conf.path_config import path_model_dir +from keras_bert import Tokenizer +import numpy as np +import macropodus +import codecs +import pickle +import os + + +path_dir = path_model_dir # + "/ner_albert_bilstm_people_199801" +# 加载模型结构 +model = model_from_json(open(path_dir+"/graph.json", "r", encoding="utf-8").read(), + custom_objects=macropodus.custom_objects) +# 加载模型权重 +model.load_weights(path_dir+"/model.h5") + +# reader tokenizer +token_dict = {} +path_dict = os.path.join(path_embedding_albert, "vocab.txt") +with codecs.open(path_dict, 'r', 'utf8') as reader: + for line in reader: + token = line.strip() + token_dict[token] = len(token_dict) +vocab_size = len(token_dict) +tokenizer = Tokenizer(token_dict) +# params +path_params = path_dir + "/params.json" +params = load_json(path_params) +len_max = params["len_max"] +# l2i_i2l +path_l2i_i2l = path_dir + "/l2i_i2l.json" +l2i_i2l = load_json(path_l2i_i2l) + + +def sentence2idx(text): + text = extract_chinese(str(text).upper()) + text = list(text) + text = [text_one for text_one in text] + len_leave = len_max - len(text) + if len_leave >= 0: + text_index = [token_dict[text_char] if text_char in token_dict else token_dict['[UNK]'] for + text_char in text] + [token_dict['[PAD]'] for i in range(len_leave)] + else: + text_index = [token_dict[text_char] if text_char in token_dict else token_dict['[UNK]'] for + text_char in text[0:len_max]] + input_mask = min(len(text), len_max) + x = [[text_index, input_mask]] + x_ = np.array(x) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + return [x_1, x_2] + +while True: + print("请输入:") + ques = input() + mode_input = sentence2idx(ques) + res = model.predict(mode_input) + res_list = res.tolist()[0] + res_idxs = [np.argmax(rl) for rl in res_list] + res_label = [l2i_i2l["i2l"][str(ri)] if str(ri) in l2i_i2l["i2l"] else "O" for ri in res_idxs] + print(res_label[:len(ques)]) + diff --git a/macropodus/network/preprocess/__init__.py b/macropodus/network/preprocess/__init__.py new file mode 100644 index 0000000..f5d5e8b --- /dev/null +++ b/macropodus/network/preprocess/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:35 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/preprocess/preprocess_generator.py b/macropodus/network/preprocess/preprocess_generator.py new file mode 100644 index 0000000..9083b6c --- /dev/null +++ b/macropodus/network/preprocess/preprocess_generator.py @@ -0,0 +1,306 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/2 21:08 +# @author : Mo +# @function: preprocess of network + + +from tensorflow.python.keras.utils import to_categorical +from macropodus.preprocess.tools_common import load_json +from macropodus.preprocess.tools_common import save_json +import numpy as np +import json +import os + + +class PreprocessGenerator: + """ + 数据预处理, 输入为csv格式, [label,ques] + """ + def __init__(self, path_model_l2i_i2l): + self.path_model_l2i_i2l = path_model_l2i_i2l + self.l2i_i2l = None + if os.path.exists(self.path_model_l2i_i2l): + self.l2i_i2l = load_json(self.path_model_l2i_i2l) + + def prereocess_idx2label(self, pred): + """ + 类标(idx)转类别(label) + :param pred: + :return: + """ + if os.path.exists(self.path_model_l2i_i2l): + pred_i2l = {} + i2l = self.l2i_i2l['i2l'] + for i in range(len(pred)): + pred_i2l[i2l[str(i)]] = pred[i] + pred_i2l_rank = [sorted(pred_i2l.items(), key=lambda k: k[1], reverse=True)] + return pred_i2l_rank + else: + raise RuntimeError("path_fast_text_model_label2index is None") + + def prereocess_label2idx(self, pred): + """ + 类别(label)转类标(idx) + :param pred: + :return: + """ + if os.path.exists(self.path_model_l2i_i2l): + pred_l2i = {} + l2i = self.l2i_i2l['l2i'] + for i in range(len(pred)): + pred_l2i[pred[i]] = l2i[pred[i]] + pred_l2i_rank = [sorted(pred_l2i.items(), key=lambda k: k[1], reverse=True)] + return pred_l2i_rank + else: + raise RuntimeError("path_fast_text_model_label2index is None") + + def preprocess_label2set(self, path): + """ + 统计label个数, 以及具体的存在 + :param path: str, like 'train.json' + :return: + """ + # 首先获取label,set,即存在的具体类 + label_sets = set([""]) + len_all = 0 + file_csv = open(path, "r", encoding="utf-8") + for line in file_csv: + len_all += 1 + if line.strip(): + ques_label = json.loads(line.strip()) + label_org = ques_label["label"] + label_sets = label_sets | set(label_org) + + file_csv.close() + return label_sets, len_all + + def preprocess_label_question_to_idx_fit_generator(self, embedding_type, batch_size, path, embed, rate=1): + """ + fit_generator用, 将句子, 类标转化为数字idx + :param embedding_type: str, like 'albert' + :param batch_size: int, like 64 + :param path: str, like 'train.json' + :param embed: class, like embed + :param rate: float, like 0.9 + :return: yield + """ + # 首先获取label,set,即存在的具体类 + label_set, len_all = self.preprocess_label2set(path) + # 获取label转index字典等, 如果label2index存在则不转换了, dev验证集合的时候用 + if not os.path.exists(self.path_model_l2i_i2l): + count = 0 + label2index = {} + index2label = {} + for label_one in label_set: + label2index[label_one] = count + index2label[count] = label_one + count = count + 1 + l2i_i2l = {} + l2i_i2l['l2i'] = label2index + l2i_i2l['i2l'] = index2label + save_json(l2i_i2l, self.path_model_l2i_i2l) + else: + l2i_i2l = load_json(self.path_model_l2i_i2l) + + # 读取数据的比例 + len_ql = int(rate * len_all) + if len_ql <= 500: # sample时候不生效,使得语料足够训练 + len_ql = len_all + + def process_line(line, embed, use_len_seq=True): + """ + 关键:对每一条数据操作，获取label和问句index + :param line: str, like '大漠帝国' + :param embed: class, like embed + :param use_len_seq: boolean, True or False + :return: + """ + + ques_label = json.loads(line.strip()) + label_org = ques_label["label"] + label_index = [l2i_i2l["l2i"][lr] for lr in label_org] + len_sequence = len(label_index) + que_embed = embed.sentence2idx(ques_label["question"]) + # padding label + len_leave = embed.len_max - len(label_index) + if len_leave >= 0: + label_index_leave = [li for li in label_index] + [l2i_i2l["l2i"]["O"] for i in range(len_leave)] + else: + label_index_leave = label_index[0:embed.len_max] + if use_len_seq: + return [que_embed[0], que_embed[1], len_sequence], label_index_leave + else: + return [que_embed, len_sequence], label_index_leave + + while True: + file_csv = open(path, "r", encoding="utf-8") + cout_all_line = 0 + cnt = 0 + x, y = [], [] + # 跳出循环 + if len_ql < cout_all_line: + break + for line in file_csv: + cout_all_line += 1 + if line.strip(): + x_line, y_line = process_line(line, embed, use_len_seq=True) + x.append(x_line) + y.append(y_line) + cnt += 1 + if cnt == batch_size: + if embedding_type in ['bert', 'albert']: + x_, y_ = np.array(x), np.array(y) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + x_3 = np.array([x[2] for x in x_]) + x_all = [x_1, x_2, x_3] + else: + x_all, y_ = np.array(x), np.array(y) + + cnt = 0 + yield (x_all, y_) + x, y =[], [] + file_csv.close() + print("preprocess_label_ques_to_idx ok") + + def preprocess_label_question_to_idx_fit(self, embedding_type, path, embed, rate=1, batch_size=64, crf_mode='reg', fit_type='fit'): + """ + fit用, 关键:对每一条数据操作，获取label和问句index + :param embedding_type: str, like 'albert' + :param path: str, like 'train.json' + :param embed: class, like embed + :param rate: float, like 0.9 + :param batch_size: int, like 64 + :param crf_mode: str, like 'reg', 'pad' + :param fit_type: str, like 'fit', 'fit_generator' + :return: np.array + """ + # 首先获取label,set,即存在的具体类 + label_set, len_all = self.preprocess_label2set(path) + # 获取label转index字典等, 如果label2index存在则不转换了, dev验证集合的时候用 + if not os.path.exists(self.path_model_l2i_i2l): + count = 0 + label2index = {} + index2label = {} + for label_one in label_set: + label2index[label_one] = count + index2label[count] = label_one + count = count + 1 + l2i_i2l = {} + l2i_i2l['l2i'] = label2index + l2i_i2l['i2l'] = index2label + save_json(l2i_i2l, self.path_model_l2i_i2l) + else: + l2i_i2l = load_json(self.path_model_l2i_i2l) + + # 读取数据的比例 + len_ql = int(rate * len_all) + if len_ql <= 500: # sample时候不生效,使得语料足够训练 + len_ql = len_all + + def process_line(line, embed, l2i_i2l): + """ + 对每一条数据操作，获取label和问句index + :param line: + :param embed: + :param l2i_i2l: + :return: + """ + # 对每一条数据操作，对question和label进行padding + ques_label = json.loads(line.strip()) + label_org = ques_label["label"] + label_index = [l2i_i2l["l2i"][lr] for lr in label_org] + # len_sequence = len(label_index) + que_embed = embed.sentence2idx("".join(ques_label["question"])) + # label padding + if embedding_type in ['bert', 'albert']: + # padding label + len_leave = embed.len_max - len(label_index) -2 + if len_leave >= 0: + label_index_leave = [l2i_i2l["l2i"][""]] + [li for li in label_index] + [l2i_i2l["l2i"][""]] + [l2i_i2l["l2i"][""] for i in range(len_leave)] + else: + label_index_leave = [l2i_i2l["l2i"][""]] + label_index[0:embed.len_max-2] + [l2i_i2l["l2i"][""]] + else: + # padding label + len_leave = embed.len_max - len(label_index) # -2 + if len_leave >= 0: + label_index_leave = [li for li in label_index] + [l2i_i2l["l2i"][""] for i in range(len_leave)] + else: + label_index_leave = label_index[0:embed.len_max] + # 转为one-hot + label_res = to_categorical(label_index_leave, num_classes=len(l2i_i2l["l2i"])) + return que_embed, label_res + + file_csv = open(path, "r", encoding="utf-8") + cout_all_line = 0 + cnt = 0 + x, y = [], [] + for line in file_csv: + # 跳出循环 + if len_ql < cout_all_line: + break + cout_all_line += 1 + if line.strip(): + # 一个json一个json处理 + # 备注:最好训练前先处理,使得ques长度小于等于len_max(word2vec), len_max-2(bert, albert) + x_line, y_line = process_line(line, embed, l2i_i2l) + x.append(x_line) + y.append(y_line.tolist()) + cnt += 1 + # 使用fit_generator时候, 每个batch_size进行yield + if fit_type=='fit_generator' and cnt == batch_size: + # 通过两种方式处理: 1.嵌入类型(bert, word2vec, random), 2.条件随机场(CRF:'pad', 'reg')类型 + if embedding_type in ['bert', 'albert']: + x_, y_ = np.array(x), np.array(y) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + x_3 = np.array([x[2] for x in x_]) + if crf_mode == 'pad': + x_all = [x_1, x_2, x_3] + elif crf_mode == 'reg': + x_all = [x_1, x_2] + else: + x_all = [x_1, x_2] + else: + x_, y_ = np.array(x), np.array(y) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + if crf_mode == 'pad': + x_all = [x_1, x_2] + elif crf_mode == 'reg': + x_all = [x_1] + else: + x_all = [x_1] + + cnt = 0 + yield (x_all, y_) + x, y =[], [] + # 使用fit的时候, return返回 + if fit_type=='fit': + # 通过两种方式处理: 1.嵌入类型(bert, word2vec, random), 2.条件随机场(CRF:'pad', 'reg')类型 + if embedding_type in ['bert', 'albert']: + x_, y_ = np.array(x), np.array(y) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + x_3 = np.array([x[2] for x in x_]) + if crf_mode=='pad': + x_all = [x_1, x_2, x_3] + elif crf_mode=='reg': + x_all = [x_1, x_2] + else: + x_all = [x_1, x_2] + else: + x_, y_ = np.array(x), np.array(y) + x_1 = np.array([x[0] for x in x_]) + x_2 = np.array([x[1] for x in x_]) + if crf_mode=='pad': + x_all = [x_1, x_2] + elif crf_mode=='reg': + x_all = x_1 + else: + x_all = x_1 + # 使用fit的时候, return返回 + return x_all, y_ + + diff --git a/macropodus/network/train/__init__.py b/macropodus/network/train/__init__.py new file mode 100644 index 0000000..dd0db4c --- /dev/null +++ b/macropodus/network/train/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 22:18 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/network/train/train_bilstm.py b/macropodus/network/train/train_bilstm.py new file mode 100644 index 0000000..741b2c2 --- /dev/null +++ b/macropodus/network/train/train_bilstm.py @@ -0,0 +1,101 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/16 22:11 +# @author : Mo +# @function: + + +# 适配linux +import pathlib +import sys +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +os.environ['TF_KERAS'] = '1' +project_path = str(pathlib.Path(os.path.abspath(__file__)).parent.parent.parent) +sys.path.append(project_path) +# 模型图 +# from macropodus.network.graph.bilstm_crf import BilstmCRFGraph as Graph +from macropodus.network.graph.bilstm import BiLSTMGraph as Graph +# 数据预处理, 删除文件目录下文件 +from macropodus.preprocess.tools_common import delete_file +# 地址 +# from macropodus.conf.path_config import path_model_dir, path_ner_people_1998_train, path_ner_people_1998_valid +from macropodus.conf.path_config import path_model_dir, path_seg_pku_1998_train, path_ner_people_1998_train, path_ner_people_1998_valid +# 计算时间 +import time + + + +def train(hyper_parameters=None, rate=1.0): + # path_ner_people_1998_train = "D:/soft_install/dataset/corpus/ner/china-people-daily-ner-corpus/example.train" + # path_ner_people_1998_valid = "D:/soft_install/dataset/corpus/ner/china-people-daily-ner-corpus/example.dev" + if not hyper_parameters: + hyper_parameters = { + 'len_max': 128, # 句子最大长度, 固定推荐20-50, bert越长会越慢, 占用空间也会变大, 本地win10-4G设为20就好, 过大小心OOM + 'embed_size': 768, # 768, # 字/词向量维度, bert取768, word取300, char可以更小些 + 'vocab_size': 20000, # 这里随便填的，会根据代码里修改 + 'trainable': False, # embedding是静态的还是动态的, 即控制可不可以微调 + 'level_type': 'char', # 级别, 最小单元, 字/词, 填 'char' or 'word', 注意:word2vec模式下训练语料要首先切好 + 'embedding_type': 'albert', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec" + 'gpu_memory_fraction': 0.76, #gpu使用率 + 'model': {'label': 4, # 类别数 + 'batch_size': 256, # 批处理尺寸, 感觉原则上越大越好,尤其是样本不均衡的时候, batch_size设置影响比较大 + 'dropout': 0.5, # 随机失活, 概率 + 'decay_step': 100, # 学习率衰减step, 每N个step衰减一次 + 'decay_rate': 0.9, # 学习率衰减系数, 乘法 + 'epochs': 132, # 训练最大轮次 + 'patience': 3, # 早停,2-3就好 + 'lr': 1e-3, # 学习率, bert取5e-5,其他取1e-3,如果acc较低或者一直不变,优先调这个, 对训练会有比较大的影响, 如果准确率一直上不去,可以考虑调这个参数 + 'l2': 1e-9, # l2正则化 + 'activate_classify': 'softmax', # 最后一个layer, 即分类激活函数 + 'loss': 'categorical_crossentropy', # 损失函数 + 'metrics': 'accuracy', # 保存更好模型的评价标准 + 'optimizer_name': 'ADAM', + 'is_training': True, # 训练后者是测试模型 + 'path_model_dir': path_model_dir, + 'model_path': os.path.join(path_model_dir, "bilstm_crf.model"), # 模型地址, loss降低则保存的依据, save_best_only=True, save_weights_only=True + 'path_hyper_parameters': os.path.join(path_model_dir, "hyper_parameters.json"), # 模型(包括embedding)，超参数地址, + 'path_fineture': os.path.join(path_model_dir, "embedding.model"), # embedding trainable地址, 例如字向量、词向量、bert向量等 + 'path_l2i_i2l':os.path.join(path_model_dir, "l2i_i2l.json"), + 'num_rnn_layers': 1, # rnn层数 + 'rnn_type': 'GRU', # rnn类型,可以填"LSTM","GRU","CuDNNLSTM","CuDNNGRU" + 'rnn_units': 256, # rnn隐藏元 + }, + 'embedding': {'layer_indexes': [12], # bert取的层数 + # 'corpus_path': '', # embedding预训练数据地址,不配则会默认取conf里边默认的地址, keras-bert可以加载谷歌版bert,百度版ernie(需转换，https://github.com/ArthurRizar/tensorflow_ernie),哈工大版bert-wwm(tf框架，https://github.com/ymcui/Chinese-BERT-wwm) + }, + 'data':{'train_data': path_seg_pku_1998_train, # path_ner_people_1998_train, # 训练数据 + 'val_data': path_seg_pku_1998_train # path_ner_people_1998_valid # 验证数据 + }, + } + + # 删除先前存在的模型和embedding微调模型等 + delete_file(path_model_dir) + time_start = time.time() + # graph初始化 + graph = Graph(hyper_parameters) + print("graph init ok!") + ra_ed = graph.word_embedding + + from macropodus.network.preprocess.preprocess_generator import PreprocessGenerator + + pg = PreprocessGenerator(os.path.join(path_model_dir, "l2i_i2l.json")) + _, len_train = pg.preprocess_label2set(hyper_parameters['data']['train_data']) + x_train, y_train = pg.preprocess_label_question_to_idx_fit(embedding_type=hyper_parameters['embedding_type'], + path=hyper_parameters['data']['train_data'], + embed=ra_ed, + rate=rate) + + x_val, y_val = pg.preprocess_label_question_to_idx_fit(embedding_type=hyper_parameters['embedding_type'], + path=hyper_parameters['data']['train_data'], + embed=ra_ed, + rate=rate) + # 训练 + graph.fit(x_train, y_train, x_val, y_val) + print("耗时:" + str(time.time()-time_start)) + + +if __name__=="__main__": + train(rate=1) diff --git a/macropodus/network/train/train_bilstm_crf.py b/macropodus/network/train/train_bilstm_crf.py new file mode 100644 index 0000000..7fdc356 --- /dev/null +++ b/macropodus/network/train/train_bilstm_crf.py @@ -0,0 +1,114 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/16 22:11 +# @author : Mo +# @function: train bilstm-crf-?embedding + + +# 适配linux +import pathlib +import sys +import os + +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +os.environ['TF_KERAS'] = '1' +project_path = str(pathlib.Path(os.path.abspath(__file__)).parent.parent.parent) +sys.path.append(project_path) +# 模型图 +# from macropodus.network.graph.bilstm_crf import BilstmCRFGraph as Graph +from macropodus.network.graph.bilstm_crf import BilstmCRFGraph as Graph +# 数据预处理, 删除文件目录下文件 +from macropodus.preprocess.tools_common import delete_file +# 地址 +from macropodus.conf.path_config import path_model_dir, path_seg_pku_1998_train, path_ner_people_1998_train, \ + path_ner_people_1998_valid +# 计算时间 +import time + + +def train(hyper_parameters=None, rate=1.0): + # path_ner_people_1998_train = "D:/soft_install/dataset/corpus/ner/china-people-daily-ner-corpus/example.train" + # path_ner_people_1998_valid = "D:/soft_install/dataset/corpus/ner/china-people-daily-ner-corpus/example.dev" + if not hyper_parameters: + hyper_parameters = { + 'len_max': 128, # 句子最大长度, 固定推荐20-50, bert越长会越慢, 占用空间也会变大, 本地win10-4G设为20就好, 过大小心OOM + 'embed_size': 300, # 768, # 字/词向量维度, bert取768, word取300, char可以更小些 + 'vocab_size': 20000, # 这里随便填的，会根据代码里修改 + 'trainable': False, # embedding是静态的还是动态的, 即控制可不可以微调 + 'level_type': 'char', # 级别, 最小单元, 字/词, 填 'char' or 'word', 注意:word2vec模式下训练语料要首先切好 + 'embedding_type': 'word2vec', # 级别, 嵌入类型, 还可以填'xlnet'、'random'、 'bert'、 'albert' or 'word2vec" + 'gpu_memory_fraction': 0.76, # gpu使用率 + 'model': {'label': 5, # 类别数 + 'batch_size': 256, # 批处理尺寸, 感觉原则上越大越好,尤其是样本不均衡的时候, batch_size设置影响比较大 + 'dropout': 0.5, # 随机失活, 概率 + 'decay_step': 100, # 学习率衰减step, 每N个step衰减一次 + 'decay_rate': 0.9, # 学习率衰减系数, 乘法 + 'epochs': 132, # 训练最大轮次 + 'patience': 3, # 早停,2-3就好 + 'lr': 1e-3, # 学习率, bert取5e-5,其他取1e-3,如果acc较低或者一直不变,优先调这个, 对训练会有比较大的影响, 如果准确率一直上不去,可以考虑调这个参数 + 'l2': 0.32, # l2正则化 + 'activate_rnn': 'tanh', # rnn-layer中的激活函数, 即RNN激活函数, 可填'tanh', 'relu', 'signmoid' + 'activate_classify': 'softmax', # 最后一个layer, 即分类激活函数, 'softmax', 'signmoid' + 'loss': 'sparse_categorical_accuracy', + # 损失函数, mse, categorical_crossentropy, sparse_categorical_crossentropy, binary_crossentropy等 + 'metrics': 'accuracy', + # 保存更好模型的评价标准, accuracy, binary_accuracy, categorical_accuracy, sparse_categorical_accuracy, sparse_top_k_categorical_accuracy + 'optimizer_name': 'ADAM', # 可填'ADAM', 'RADAM', 'RADAM,LOOKAHEAD' + 'is_training': True, # 训练后者是测试模型, 训练时候是True, 测试时候是False + 'path_model_dir': path_model_dir, # 保存当前训练模型的根目录 + 'model_path': os.path.join(path_model_dir, "bilstm_crf.model"), + # 模型地址, loss降低则保存的依据, save_best_only=True, save_weights_only=True + 'path_hyper_parameters': os.path.join(path_model_dir, "hyper_parameters.json"), + # 模型(包括embedding)，超参数地址, + 'path_fineture': os.path.join(path_model_dir, "embedding.model"), + # embedding trainable地址, 例如字向量、词向量、bert向量等 + 'path_l2i_i2l': os.path.join(path_model_dir, "l2i_i2l.json"), # 类别转类标的字典 + 'num_rnn_layers': 1, # rnn层数, 1, 2 or 3等 + 'rnn_type': 'GRU', # rnn类型,可以填"LSTM","GRU","CuDNNLSTM","CuDNNGRU" + 'rnn_units': 256, # rnn隐藏元, 128, 256, 512, 768, 1024等 + 'crf_mode': 'reg', # crf类型, 可填'reg', 'pad'(包括句子实际长度) + }, + 'embedding': {'layer_indexes': [12], # bert取的层数 + # 'corpus_path': '', # embedding预训练数据地址,不配则会默认取conf里边默认的地址, keras-bert可以加载谷歌版bert,百度版ernie(需转换，https://github.com/ArthurRizar/tensorflow_ernie),哈工大版bert-wwm(tf框架，https://github.com/ymcui/Chinese-BERT-wwm) + }, + 'data': {'train_data': path_seg_pku_1998_train, # path_ner_people_1998_train, # 训练数据 + 'val_data': path_seg_pku_1998_train # path_ner_people_1998_valid # 验证数据 + }, + } + + # 删除先前存在的模型和embedding微调模型等 + delete_file(path_model_dir) + time_start = time.time() + # graph初始化 + graph = Graph(hyper_parameters) + print("graph init ok!") + ra_ed = graph.word_embedding + + from macropodus.network.preprocess.preprocess_generator import PreprocessGenerator + + pg = PreprocessGenerator(os.path.join(path_model_dir, "l2i_i2l.json")) + _, len_train = pg.preprocess_label2set(hyper_parameters['data']['train_data']) + x_train, y_train = pg.preprocess_label_question_to_idx_fit(embedding_type=hyper_parameters['embedding_type'], + path=hyper_parameters['data']['train_data'], + embed=ra_ed, + rate=rate, + batch_size=hyper_parameters['model']['batch_size'], + crf_mode=hyper_parameters['model']['crf_mode'], + fit_type='fit') + + x_val, y_val = pg.preprocess_label_question_to_idx_fit(embedding_type=hyper_parameters['embedding_type'], + path=hyper_parameters['data']['train_data'], + embed=ra_ed, + rate=rate, + batch_size=hyper_parameters['model']['batch_size'], + crf_mode=hyper_parameters['model']['crf_mode'], + fit_type='fit') + # 训练 + graph.fit(x_train, y_train, x_val, y_val) + print("耗时:" + str(time.time() - time_start)) + + +if __name__ == "__main__": + train(rate=0.1) diff --git a/macropodus/preprocess/__init__.py b/macropodus/preprocess/__init__.py new file mode 100644 index 0000000..ba99672 --- /dev/null +++ b/macropodus/preprocess/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 10:39 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/preprocess/tools_clear.py b/macropodus/preprocess/tools_clear.py new file mode 100644 index 0000000..553cdbd --- /dev/null +++ b/macropodus/preprocess/tools_clear.py @@ -0,0 +1,26 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:02 +# @author : Mo +# @function: clear text + + +def is_total_num(text): + """ + 判断是否是数字的 + :param text: str + :return: boolean, True or false + """ + try: + text_clear = text.replace(" ", "").strip() + number = 0 + for one in text_clear: + if one.isdigit(): + number += 1 + if number == len(text_clear): + return True + else: + return False + except: + return False + diff --git a/macropodus/preprocess/tools_common.py b/macropodus/preprocess/tools_common.py new file mode 100644 index 0000000..9fd7645 --- /dev/null +++ b/macropodus/preprocess/tools_common.py @@ -0,0 +1,139 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 0:15 +# @author : Mo +# @function: common tools of macropodus + + +from macropodus.conf.path_log import get_logger_root +import json +import os +import re + + +re_continue = re.compile('[A-Za-z0-9.@_]', re.U) + + +logger = get_logger_root() + + +__all__ = ["txt_read", + "txt_write", + "save_json", + "load_json", + "delete_file"] + + +def txt_read(path_file, encode_type='utf-8'): + """ + 读取txt文件，默认utf8格式, 不能有空行 + :param file_path: str, 文件路径 + :param encode_type: str, 编码格式 + :return: list + """ + list_line = [] + try: + file = open(path_file, 'r', encoding=encode_type) + while True: + line = file.readline().strip() + if not line: + break + list_line.append(line) + file.close() + except Exception as e: + logger.info(str(e)) + finally: + return list_line + + +def txt_write(list_line, file_path, type='w', encode_type='utf-8'): + """ + txt写入list文件 + :param listLine:list, list文件，写入要带"\n" + :param filePath:str, 写入文件的路径 + :param type: str, 写入类型, w, a等 + :param encode_type: + :return: + """ + try: + file = open(file_path, type, encoding=encode_type) + file.writelines(list_line) + file.close() + except Exception as e: + logger.info(str(e)) + + +def save_json(json_lines, json_path): + """ + 保存json， + :param json_lines: json + :param path: str + :return: None + """ + with open(json_path, 'w', encoding='utf-8') as fj: + fj.write(json.dumps(json_lines, ensure_ascii=False)) + fj.close() + + +def load_json(path): + """ + 获取json, json存储为[{}]格式, like [{'大漠帝国':132}] + :param path: str + :return: json + """ + with open(path, 'r', encoding='utf-8') as fj: + model_json = json.load(fj) + return model_json + + +def delete_file(path): + """ + 删除一个目录下的所有文件 + :param path: str, dir path + :return: None + """ + for i in os.listdir(path): + # 取文件或者目录的绝对路径 + path_children = os.path.join(path, i) + if os.path.isfile(path_children): + if path_children.endswith(".h5") or path_children.endswith(".json"): + os.remove(path_children) + else:# 递归, 删除目录下的所有文件 + delete_file(path_children) + + +def get_dir_files(path_dir): + """ + 递归获取某个目录下的所有文件(单层) + :param path_dir: str, like '/home/data' + :return: list, like ['2019_12_5.txt'] + """ + + def get_dir_files_func(file_list, dir_list, root_path=path_dir): + """ + 递归获取某个目录下的所有文件 + :param root_path: str, like '/home/data' + :param file_list: list, like [] + :param dir_list: list, like [] + :return: None + """ + # 获取该目录下所有的文件名称和目录名称 + dir_or_files = os.listdir(root_path) + for dir_file in dir_or_files: + # 获取目录或者文件的路径 + dir_file_path = os.path.join(root_path, dir_file) + # 判断该路径为文件还是路径 + if os.path.isdir(dir_file_path): + dir_list.append(dir_file_path) + # 递归获取所有文件和目录的路径 + get_dir_files_func(dir_file_path, file_list, dir_list) + else: + file_list.append(dir_file_path) + + # 用来存放所有的文件路径 + _files = [] + # 用来存放所有的目录路径 + dir_list = [] + get_dir_files_func(_files, dir_list, path_dir) + return _files + diff --git a/macropodus/preprocess/tools_ml.py b/macropodus/preprocess/tools_ml.py new file mode 100644 index 0000000..e349735 --- /dev/null +++ b/macropodus/preprocess/tools_ml.py @@ -0,0 +1,192 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 20:23 +# @author : Mo +# @function: data utils of ml, text_summarization + + +from sklearn.feature_extraction.text import TfidfTransformer +from sklearn.feature_extraction.text import TfidfVectorizer +import jieba.posseg as pseg +import macropodus +import re + + +__all__ = ["extract_chinese", + "macropodus_cut", + "jieba_tag_cut", + "cut_sentence", + "remove_urls", + "tfidf_fit", + "tfidf_sim" + ] + + +def extract_chinese(text): + """ + 只提取出中文、字母和数字 + :param text: str, input of sentence + :return: str + """ + chinese_exttract = ''.join(re.findall(u"([\u4e00-\u9fa5A-Za-z0-9@. ])", text)) + return chinese_exttract + + +def jieba_tag_cut(text): + """ + jieba cut and tagged + :param text:str + :return: dict + """ + words = pseg.cut(text) + return dict(words) + + +def macropodus_cut(text): + """ + Macropodus cut + :param text: input sentence + :return: list + """ + return macropodus.cut_dag(text) + + +def cut_sentence(text, use_type="summarize"): + """ + 分句(文本摘要) + :param sentence:str, like "大漠帝国" + :param use_type:str, like "summarize" or "new-word-discovery" + :return:list + """ + if use_type=="summarize": + re_sen = re.compile('[:;!?。：；？！\n\r]') #.不加是因为不确定.是小数还是英文句号(中文省略号......) + elif use_type=="new-word-discovery": + re_sen = re.compile('[,，"“”、<>《》{}【】:;!?。：；？！\n\r]') #.不加是因为不确定.是小数还是英文句号(中文省略号......) + else: + raise RuntimeError("use_type must be 'summarize' or 'new-word-discovery'") + sentences = re_sen.split(text) + sen_cuts = [] + for sen in sentences: + if sen and str(sen).strip(): + sen_cuts.append(sen) + return sen_cuts + + +def remove_urls(text): + """ + 删除https/http等无用url + :param text: str + :return: str + """ + text_remove_url = re.sub(r'(全文：)?(https|http)?:\/\/(\w|\.|\/|\?|\=|\&|\%)*\b', + '', text, flags=re.MULTILINE) + return text_remove_url + + +def gram_uni_bi_tri(text): + """ + 获取文本的unigram, trugram, bigram等特征 + :param text: str + :return: list + """ + len_text = len(text) + gram_uni = [] + gram_bi = [] + gram_tri = [] + for i in range(len_text): + if i + 3 <= len_text: + gram_uni.append(text[i]) + gram_bi.append(text[i:i+2]) + gram_tri.append(text[i:i+3]) + elif i + 2 <= len_text: + gram_uni.append(text[i]) + gram_bi.append(text[i:i+2]) + elif i + 1 <= len_text: + gram_uni.append(text[i]) + else: + break + return gram_uni, gram_bi, gram_tri + + +def get_ngrams(text, ns=[1], use_type="summarize", len_max=7): + """ + 获取文本的ngram等特征 + :param text: str, like "大漠帝国" + :param ns: list, like [1, 2, 3] + :param type: str, like "summarize" or "new-word-discovery" + :param type: int, like 6, 7 + :return: list or list + """ + if type(ns) != list: + raise RuntimeError("ns of function get_ngram() must be list!") + for n in ns: + if n < 1: + raise RuntimeError("enum of ns must '>1'!") + len_text = len(text) + ngrams = [] + if use_type == "summarize": # 分别返回uni, bi, tri... + for n in ns: + ngram_n = [] + for i in range(len_text): + if i + n <= len_text: + ngram_n.append(text[i:i + n]) + else: + break + if not ngram_n: + ngram_n.append(text) + ngrams += ngram_n + else: # 只返回一个list + for i in range(len_text): + ngrams += [text[i: j + i] + for j in range(1, min(len_max + 1, len_text - i + 1))] + return ngrams + + +def tfidf_fit(sentences): + """ + tfidf相似度 + :param sentences: str + :return: list, list, list + """ + # tfidf计算 + model = TfidfVectorizer(ngram_range=(1, 2), # 3,5 + stop_words=[' ', '\t', '\n'], # 停用词 + max_features=10000, + token_pattern=r"(?u)\b\w+\b", # 过滤停用词 + min_df=1, + max_df=0.9, + use_idf=1, # 光滑 + smooth_idf=1, # 光滑 + sublinear_tf=1, ) # 光滑 + matrix = model.fit_transform(sentences) + return matrix + + +def tdidf_sim(sentences): + """ + tfidf相似度 + :param sentences: + :return: + """ + # tfidf计算 + model = TfidfVectorizer(tokenizer=macropodus_cut, + ngram_range=(1, 2), # 3,5 + stop_words=[' ', '\t', '\n'], # 停用词 + max_features=10000, + token_pattern=r"(?u)\b\w+\b", # 过滤停用词 + min_df=1, + max_df=0.9, + use_idf=1, # 光滑 + smooth_idf=1, # 光滑 + sublinear_tf=1, ) # 光滑 + matrix = model.fit_transform(sentences) + matrix_norm = TfidfTransformer().fit_transform(matrix) + return matrix_norm + + +if __name__ == '__main__': + text = "你喜欢谁,小老弟,你好烦哇。" + # gg = jieba_tag_cut("我不再喜欢你，正如你的不喜欢我") + grams = get_ngrams(text, use_type="new-word-discovery", len_max=7) + # print(gg) + print(grams) diff --git a/macropodus/segment/__init__.py b/macropodus/segment/__init__.py new file mode 100644 index 0000000..08f8064 --- /dev/null +++ b/macropodus/segment/__init__.py @@ -0,0 +1,30 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 22:00 +# @author : Mo +# @function: segment of sent + + +from macropodus.segment.seg_statistics.seg_statistics import SegStatistics +from macropodus.segment.word_discovery.word_discovery import WordDiscovery + +# 机械分词 +use_cache = True # 使用缓存 +segs = SegStatistics(use_cache) +cut_bidirectional = segs.cut_bidirectional +cut_forward = segs.cut_forward +cut_reverse = segs.cut_reverse +cut_search = segs.cut_search +cut_dag = segs.cut_dag +cut = segs.cut + +# 用户词典增删改查 +load_user_dict = segs.load_user_dict +save_delete_words = segs.save_delete_words +save_add_words = segs.save_add_words +delete_word = segs.delete_word +add_word = segs.add_word + +# 新词发现 +wd = WordDiscovery() +find = wd.find_word diff --git a/macropodus/segment/seg_statistics/__init__.py b/macropodus/segment/seg_statistics/__init__.py new file mode 100644 index 0000000..712aba7 --- /dev/null +++ b/macropodus/segment/seg_statistics/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 9:25 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/segment/seg_statistics/seg_bidirectional.py b/macropodus/segment/seg_statistics/seg_bidirectional.py new file mode 100644 index 0000000..9141661 --- /dev/null +++ b/macropodus/segment/seg_statistics/seg_bidirectional.py @@ -0,0 +1,62 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 9:55 +# @author : Mo +# @function: cut sentences of forward of reverse of maxlength + + +from macropodus.segment.seg_statistics.seg_forward import SegForward +from macropodus.segment.seg_statistics.seg_reverse import SegReverse + + +class SegBidirectional(object): + def __init__(self): + self.seg_forward = SegForward() + self.seg_reverse = SegReverse() + + def cut(self, sentence): + """ + 最大双向词典切词, 即最大正向切词与最大反向切词合并, 选择词数小的那个返回 + :param sentence: str + :return: + """ + res_forward = self.seg_forward.cut(sentence) + res_reverse = self.seg_reverse.cut(sentence) + res_forward_list = list(res_forward) + res_reverse_list = list(res_reverse) + len_res_forward = len(res_forward_list) + len_res_reverse = len(res_reverse_list) + if len_res_forward >= len_res_reverse: + for rrl in res_reverse_list: + yield rrl + else: + for rfl in res_forward_list: + yield rfl + + +if __name__ == '__main__': + sb = SegBidirectional() + sentence = "研究生命科学研究生命科学" + print(list(sb.cut(sentence))) + + # 测试性能 + from macropodus.preprocess.tools_common import txt_read, txt_write + from macropodus.conf.path_config import path_root + import time + + path_wordseg_a = path_root.replace("macropodus", "") + "/test/tet/ambiguity.txt" + sentences = txt_read(path_wordseg_a) + + time_start = time.time() + count = 0 + for i in range(10000): + for sen in sentences: + count += 1 + res = sb.cut(sen) + # print(list(res)) + time_end = time.time() + print(time_end - time_start) + print(count/(time_end - time_start)) + # yield + # 10000/0.17*50 = 2500*50 = 2896810(line/s) + # 50000/0.90*50 = 2500000/20 = 2763600(line/s) \ No newline at end of file diff --git a/macropodus/segment/seg_statistics/seg_dag.py b/macropodus/segment/seg_statistics/seg_dag.py new file mode 100644 index 0000000..6368eff --- /dev/null +++ b/macropodus/segment/seg_statistics/seg_dag.py @@ -0,0 +1,118 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 9:58 +# @author : Mo +# @function: segmentation of maximum probability using dictionary + + +from macropodus.preprocess.tools_common import re_continue +from macropodus.base.seg_basic import SegBasic +from math import log + + +class SegDAG(SegBasic): + def __init__(self): + super().__init__() + + def build_dag(self, sentence, len_word_max=105): + """ + 构建句子的词典概率有向图; + jieba使用的是前缀字典替代前缀树,内存比前缀树小,且比前缀树快; + 基本思想是构建'大漠帝国:132','大漠帝','大漠:640','大':1024等，没有则置为0, + 搜索时候前缀不存在就跳出,不用继续下去了 + :param sentence: str, like '大漠帝国是谁' + :param sentence: int, like 132 + :return: dict, like {0:[0,1], 1:[1]} + """ + len_sen = len(sentence) + dag_sen = {} + for i in range(len_sen): # 前向遍历, 全切分 + enum_j = [i] # 单个字就是它本身 + for j in range(i+1, min(len_sen, i+len_word_max)): # 遍历从当前字到句子末尾可能成词的部分, 当前的不取, 设置最大成词长度为132 + word_maybe = sentence[i:j+1] + if word_maybe in self.dict_words_freq: + enum_j.append(j) + dag_sen[i] = enum_j + return dag_sen + + def calculate_prob(self, sentence, DAG, route): + """ + 动态规划求取最大概率, 代码来自jieba项目 + code from: https://github.com/fxsjy/jieba + :param sentence: str, input of sentence, like "大漠帝国是谁?" + :param DAG: dict, + :param route: dict, + :return: None + """ + len_sen = len(sentence) + route[len_sen] = (0, 0) + log_total = log(self.num_words) + for index in range(len_sen - 1, -1, -1): # 动态规划 + route[index] = max((log(self.dict_words_freq.get(sentence[index:x + 1]) or 1) + - log_total + route[x + 1][0], x) for x in DAG[index]) + + def cut(self, sentence): + """ + seg_dag字典最大概率切词, 代码来自jieba项目 + code from: https://github.com/fxsjy/jieba + :param sentence: str, input of sentence, like "大漠帝国是谁?" + :return: None + """ + len_sen = len(sentence) + word_temp = '' + route = {} + i = 0 + DAG = self.build_dag(sentence) # 根据sentence构建有向图dag + self.calculate_prob(sentence, DAG, route) # 动态规划计算概率最大的路径 + while i < len_sen: + j = route[i][1] + 1 # 获取index, i为成词的begin, j为成词的end + word_ch = sentence[i:j] # 概率成词 + if (j-i<2) and re_continue.match(word_ch): # 单个字判断是否为连续, 字母-数字-.-@等为连续 + word_temp += word_ch + i = j + else: # 成词后返回一个yield可迭代对象, yield后转list有点耗时 + if word_temp: # 有word_temp的情况下 word_ch也没有迭代返回 + yield word_temp + word_temp = '' + yield word_ch + i = j + if word_temp: # 最后一个成词为"字母-数字-.-@等为连续"的情况 + yield word_temp + + +if __name__ == '__main__': + sd = SegDAG() + sd.add_word(str('知识图谱')) + + # for i in range(50000): + sd_enum = sd.cut(sentence='apple_pir大漠帝国我再也找不到了') + print(list(sd_enum)) + + # 测试性能 + from macropodus.preprocess.tools_common import txt_read, txt_write + from macropodus.conf.path_config import path_root + import time + path_wordseg_a = path_root.replace("macropodus", "") + "/test/tet/ambiguity.txt" + sentences = txt_read(path_wordseg_a) + + time_start = time.time() + count = 0 + for i in range(10000): + for sen in sentences: + # print("原句:"+sen) + count += 1 + res = sd.cut(sen) + # print(list(res)) + time_end = time.time() + print(time_end-time_start) + print(count/(time_end - time_start)) + +while True: + print("请输入:") + sen = input() + print(list(sd.cut(sen))) +# win10测试, i7 8th + 16G RAM +# 10000/0.17*50 = 2864136(line/s) +# 50000/0.87*50 = 2872092(line/s) + + diff --git a/macropodus/segment/seg_statistics/seg_forward.py b/macropodus/segment/seg_statistics/seg_forward.py new file mode 100644 index 0000000..402aed6 --- /dev/null +++ b/macropodus/segment/seg_statistics/seg_forward.py @@ -0,0 +1,65 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 9:54 +# @author : Mo +# @function: cut sentences of forward of maxlength + + +from macropodus.base.seg_basic import SegBasic + + +class SegForward(SegBasic): + def __init__(self): + super().__init__() + + def cut(self, sentence, len_max=7): + """ + 正向最大切词 + :param sentence: str, like '大漠帝国' + :param len_max: int, like 32 + :return: yield + """ + len_sen = len(sentence) + i = 0 + while i < len_sen: # while判断条件 + flag = False # flag标志位,确定有没有在字典里边的单字词或多字词 + for j in range(min(len_sen+1, i+len_max), -i, -1): # 遍历从当前字到句子末尾可能成词的部分, 从最后i+len_max算起 + word_maybe = sentence[i:j] # 正向可能成词的语 + if word_maybe in self.dict_words_freq: # 是否在字典里边 + i = j # 成词前标志i向后移动 + flag = True # flag标志位变化 + yield word_maybe + break # 成词则跳出循环 + if not flag: # 未选中后单个字的情况 + yield sentence[i] + i += 1 + +if __name__ == '__main__': + sf = SegForward() + sentence = "macropodus是啥子呢" + sentence = "方程的解除了零以外还有…" + print(list(sf.cut(sentence))) + + # 测试性能 + from macropodus.preprocess.tools_common import txt_read, txt_write + from macropodus.conf.path_config import path_root + import time + + path_wordseg_a = path_root.replace("macropodus", "") + "/test/tet/ambiguity.txt" + sentences = txt_read(path_wordseg_a) + + time_start = time.time() + count = 0 + for i in range(10000): + for sen in sentences: + # print(sen) + count += 1 + res = sf.cut(sen) + # print(list(res)) + time_end = time.time() + print(time_end - time_start) + print(count/(time_end - time_start)) + + # 10000/0.17*50 = 2831272(line/s) + + diff --git a/macropodus/segment/seg_statistics/seg_reverse.py b/macropodus/segment/seg_statistics/seg_reverse.py new file mode 100644 index 0000000..92e168f --- /dev/null +++ b/macropodus/segment/seg_statistics/seg_reverse.py @@ -0,0 +1,72 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/19 9:54 +# @author : Mo +# @function: cut sentences of reverse of maxlength + + +from macropodus.base.seg_basic import SegBasic + + +class SegReverse(SegBasic): + def __init__(self): + super().__init__() + + def cut(self, sentence, len_max=7): + """ + 反向最大切词 + :param sentence: str, like '大漠帝国' + :param len_max: int, like 32 + :return: yield + """ + len_sen = len(sentence) + i = len_sen + res = [] + while i > 0: # while判断条件 + flag = False # flag标志位,确定有没有在字典里边的单字词或多字词 + for j in range(max(0, i - len_max), i): # 遍历从句子末尾向前可能成词的部分, 从最后i-len_max算起 + word_maybe = sentence[j:i] # 正向可能成词的语 + if word_maybe in self.dict_words_freq: # 是否在字典里边 + i = j # 成词前标志i向后移动 + flag = True # flag标志位变化 + res.append(word_maybe) + # yield word_maybe + break # 成词则跳出循环 + if not flag: # 未选中后单个字的情况 + i -= 1 + # yield sentence[i] + res.append(sentence[i]) + for i in range(len(res)-1, 0, -1): + yield res[i] + # return res + + +if __name__ == '__main__': + a = max(0,5) + sf = SegReverse() + sentence = "研究生命科学\t研究 生命 科学" + print(list(sf.cut(sentence))) + print(list(sf.cut(""))) + + # 测试性能 + from macropodus.preprocess.tools_common import txt_read, txt_write + from macropodus.conf.path_config import path_root + import time + path_wordseg_a = path_root.replace("macropodus", "") + "/test/tet/ambiguity.txt" + sentences = txt_read(path_wordseg_a) + + time_start = time.time() + count = 0 + for i in range(50000): + for sen in sentences: + # print(sen) + count += 1 + res = (sf.cut(sen)) + # print(res) + time_end = time.time() + print(time_end-time_start) + print(count/(time_end - time_start)) + + # 10000/0.18*50 = 2500*50 = 2784226(line/s) + # 50000/0.98*50 = 2500000/20 = 2550109(line/s) + diff --git a/macropodus/segment/seg_statistics/seg_statistics.py b/macropodus/segment/seg_statistics/seg_statistics.py new file mode 100644 index 0000000..4c20714 --- /dev/null +++ b/macropodus/segment/seg_statistics/seg_statistics.py @@ -0,0 +1,222 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/3 20:01 +# @author : Mo +# @function: segnment of statistics + + +from macropodus.preprocess.tools_common import re_continue +from macropodus.base.seg_basic import SegBasic +from math import log + + +__all__ = ["cut_dag", + "cut_forward", + "cut_reverse", + "cut_bidirectional", + "cut_search"] + + +class SegStatistics(SegBasic): + def __init__(self, use_cache): + self.algorithm = "chinese-word-segnment" + super().__init__(use_cache) + + def build_dag(self, sentence, len_word_max=105): + """ + 构建句子的词典概率有向图; + jieba使用的是前缀字典替代前缀树,内存比前缀树小,且比前缀树快; + 基本思想是构建'大漠帝国:132','大漠帝','大漠:640','大':1024等，没有则置为0, + 搜索时候前缀不存在就跳出,不用继续下去了 + :param sentence: str, like '大漠帝国是谁' + :param sentence: int, like 132 + :return: dict, like {0:[0,1], 1:[1]} + """ + len_sen = len(sentence) + dag_sen = {} + for i in range(len_sen): # 前向遍历, 全切分 + enum_j = [i] # 单个字就是它本身 + for j in range(i+1, min(len_sen, i+len_word_max)): # 遍历从当前字到句子末尾可能成词的部分, 当前的不取, 设置最大成词长度为132 + word_maybe = sentence[i:j+1] + if word_maybe in self.dict_words_freq: + enum_j.append(j) + dag_sen[i] = enum_j + return dag_sen + + def calculate_prob(self, sentence, DAG, route): + """ + 动态规划求取最大概率, 代码来自jieba项目 + code from: https://github.com/fxsjy/jieba + :param sentence: str, input of sentence, like "大漠帝国是谁?" + :param DAG: dict, + :param route: dict, + :return: None + """ + len_sen = len(sentence) + route[len_sen] = (0, 0) + log_total = log(self.num_words) + for index in range(len_sen - 1, -1, -1): # 动态规划 + route[index] = max((log(self.dict_words_freq.get(sentence[index:x + 1]) or 1) + - log_total + route[x + 1][0], x) for x in DAG[index]) + + def cut_dag(self, sentence): + """ + seg_dag字典最大概率切词, 代码来自jieba项目 + code from: https://github.com/fxsjy/jieba + :param sentence: str, input of sentence, like "大漠帝国是谁?" + :return: None + """ + len_sen = len(sentence) + word_temp = '' + route = {} + i = 0 + DAG = self.build_dag(sentence) # 根据sentence构建有向图dag + self.calculate_prob(sentence, DAG, route) # 动态规划计算概率最大的路径 + while i < len_sen: + j = route[i][1] + 1 # 获取index, i为成词的begin, j为成词的end + word_ch = sentence[i:j] # 概率成词 + if (j-i<2) and re_continue.match(word_ch): # 单个字判断是否为连续, 字母-数字-.-@等为连续 + word_temp += word_ch + i = j + else: # 成词后返回一个yield可迭代对象, yield后转list有点耗时 + if word_temp: # 有word_temp的情况下 word_ch也没有迭代返回 + yield word_temp + word_temp = '' + yield word_ch + i = j + if word_temp: # 最后一个成词为"字母-数字-.-@等为连续"的情况 + yield word_temp + + def cut_forward(self, sentence, len_max=7): + """ + 正向最大切词 + :param sentence: str, like '大漠帝国' + :param len_max: int, like 32 + :return: yield + """ + len_sen = len(sentence) + i = 0 + while i < len_sen: # while判断条件 + flag = False # flag标志位,确定有没有在字典里边的单字词或多字词 + for j in range(min(len_sen+1, i+len_max), -i, -1): # 遍历从当前字到句子末尾可能成词的部分, 从最后i+len_max算起 + word_maybe = sentence[i:j] # 正向可能成词的语 + if word_maybe in self.dict_words_freq: # 是否在字典里边 + i = j # 成词前标志i向后移动 + flag = True # flag标志位变化 + yield word_maybe + break # 成词则跳出循环 + if not flag: # 未选中后单个字的情况 + yield sentence[i] + i += 1 + + def cut_reverse(self, sentence, len_max=7): + """ + 反向最大切词 + :param sentence: str, like '大漠帝国' + :param len_max: int, like 32 + :return: yield + """ + len_sen = len(sentence) + i = len_sen + res = [] + while i > 0: # while判断条件 + flag = False # flag标志位,确定有没有在字典里边的单字词或多字词 + for j in range(max(0, i - len_max), i): # 遍历从句子末尾向前可能成词的部分, 从最后i-len_max算起 + word_maybe = sentence[j:i] # 正向可能成词的语 + if word_maybe in self.dict_words_freq: # 是否在字典里边 + i = j # 成词前标志i向后移动 + flag = True # flag标志位变化 + res.append(word_maybe) + # yield word_maybe + break # 成词则跳出循环 + if not flag: # 未选中后单个字的情况 + i -= 1 + # yield sentence[i] + res.append(sentence[i]) + for i in range(len(res)-1, 0, -1): + yield res[i] + # return res + + def cut_bidirectional(self, sentence): + """ + 最大双向词典切词, 即最大正向切词与最大反向切词合并, 选择词数小的那个返回 + :param sentence: str + :return: + """ + res_forward = self.cut_forward(sentence) + res_reverse = self.cut_reverse(sentence) + res_forward_list = list(res_forward) + res_reverse_list = list(res_reverse) + len_res_forward = len(res_forward_list) + len_res_reverse = len(res_reverse_list) + if len_res_forward >= len_res_reverse: + for rrl in res_reverse_list: + yield rrl + else: + for rfl in res_forward_list: + yield rfl + + def cut_search(self, sentence): + """ + 搜索引擎切词, 全切词 + :param sentence: str, like "大漠帝国" + :return: yield + """ + DAG = self.build_dag(sentence) # 根据sentence构建有向图dag + for k, v in DAG.items(): + for vi in v: + yield sentence[k:vi+1] # 遍历无向图, 返回可能存在的所有切分 + + def cut(self, sentence, type_cut="cut_dag"): + """ + 切词总函数 + :param sentence:str, like '大漠帝国, macropodus, 中国斗鱼' + :param type_cut: str, like 'cut_dag', 'cut_forward', 'cut_reverse', 'cut_bidirectional', 'cut_search' + :return: list, like ['大漠帝国', ',', 'macropodus', ',', '中国斗鱼'] + """ + if type_cut=="cut_dag": + return list(self.cut_dag(sentence)) + elif type_cut=="cut_forward": + return list(self.cut_dag(sentence)) + elif type_cut=="cut_reverse": + return list(self.cut_dag(sentence)) + elif type_cut=="cut_bidirectional": + return list(self.cut_dag(sentence)) + elif type_cut=="cut_search": + return list(self.cut_dag(sentence)) + else: + raise RuntimeError("type_cut must be 'cut_dag', 'cut_forward', 'cut_reverse', 'cut_bidirectional', 'cut_search'") + + +if __name__ == '__main__': + sd = SegStatistics(True) + sd.add_word(str('知识图谱')) + sd_search = sd.cut_search("已结婚的和尚未结婚的青年都要实行计划生育") + print(list(sd_search)) + # for i in range(50000): + sd_enum = sd.cut_dag(sentence="已结婚的和尚未结婚的青年都要实行计划生育") + print(list(sd_enum)) + sd_enum = sd.cut_dag(sentence='what‘syournamesirareyouok!') + print(list(sd_enum)) + # 测试性能 + from macropodus.preprocess.tools_common import txt_read, txt_write + from macropodus.conf.path_config import path_root + import time + path_wordseg_a = path_root.replace("macropodus", "") + "/test/tet/ambiguity.txt" + sentences = txt_read(path_wordseg_a) + + time_start = time.time() + count = 0 + for i in range(50000): + for sen in sentences: + count += 1 + res = sd.cut_search(sen) + # print(list(res)) + time_end = time.time() + print(time_end-time_start) + print(count/(time_end - time_start)) + + +# win10测试, i7 8th + 16G RAM +# 10000/0.17*50 = 2864136(line/s) +# 50000/0.87*50 = 2872092(line/s) diff --git a/macropodus/segment/word_discovery/__init__.py b/macropodus/segment/word_discovery/__init__.py new file mode 100644 index 0000000..6e779dd --- /dev/null +++ b/macropodus/segment/word_discovery/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/19 15:36 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/segment/word_discovery/word_discovery.py b/macropodus/segment/word_discovery/word_discovery.py new file mode 100644 index 0000000..70ad390 --- /dev/null +++ b/macropodus/segment/word_discovery/word_discovery.py @@ -0,0 +1,217 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/19 16:44 +# @author : Mo +# @function: chinese word discovery + + +from macropodus.preprocess.tools_ml import cut_sentence +from macropodus.preprocess.tools_ml import get_ngrams +from collections import Counter +import math +import os + + +class WordDiscovery: + def __init__(self): + self.algorithm = "new-word-discovery" + self.total_words = 0 + self.freq_min = 3 + self.len_max = 7 + + def count_word(self, text, use_type="text"): + """ + 词频统计(句子/段落/文章) + :param text: str, path or doc, like "大漠帝国。" or "/home/data/doc.txt" + :param use_type: str, "text" or "file", file of "utf-8" of "txt" + :return: class, word-freq + """ + self.words_count = Counter() + if use_type=="text": # 输入为文本形式 + texts = cut_sentence(use_type=self.algorithm, + text=text) # 切句子, 如中英文的逗号/句号/感叹号 + for text in texts: + n_grams = get_ngrams(use_type=self.algorithm, + len_max=self.len_max, + text=text) # 获取一个句子的所有n-gram + self.words_count.update(n_grams) + elif use_type=="file": # 输入为文件形式 + if not os.path.exists(text): + raise RuntimeError("path of text must exist!") + fr8 = open(text, "r", encoding="utf-8") + for text in fr8: + if text.strip(): + texts = cut_sentence(use_type=self.algorithm, + text=text) # 切句子, 如中英文的逗号/句号/感叹号 + for text in texts: + n_grams = get_ngrams(use_type=self.algorithm, + len_max=self.len_max, + text=text) # 获取一个句子的所有n-gram + self.words_count.update(n_grams) + fr8.close() + else: + raise RuntimeError("use_type must be 'text' or 'file'") + self.total_words = sum(self.words_count.values()) + + def calculate_entropy(self, boundary_type="left"): + """ + 计算左熵和右熵 + :param boundary_type: str, like "left" or "right" + :return: None + """ + # 获取成词的最左边和最右边的一个字 + one_collect = {} + for k, v in self.words_count.items(): + len_k = len(k) + if len_k >= 3: # 词长度大于3 + if boundary_type == "right": + k_boundary = k[:-1] + else: + k_boundary = k[1:] + if k_boundary in self.words_select: # 左右边, 保存为dict + if k_boundary not in one_collect: + one_collect[k_boundary] = [v] + else: + one_collect[k_boundary] = one_collect[k_boundary] + [v] + + # 计算成词的互信息 + for k, v in self.words_select.items(): + # 从字典获取 + boundary_v = one_collect.get(k, None) + # 计算候选词的左右凝固度, 取最小的那个 + if boundary_v: + sum_boundary = sum(boundary_v) # 求和 + # 计算信息熵 + entroy_boundary = sum([-(enum_bo / sum_boundary) * math.log(enum_bo / sum_boundary) + for enum_bo in boundary_v]) + else: + entroy_boundary = 0.0 + if boundary_type == "right": + self.right_entropy[k] = entroy_boundary + else: + self.left_entropy[k] = entroy_boundary + + def compute_entropys(self): + """ + 计算凝固度 + :param words_count:dict, like {"我":32, "你们":12} + :param len_max: int, like 6 + :param freq_min: int, like 32 + :return: dict + """ + # 提取大于最大频率的词语, 以及长度在3-len_max的词语 + self.words_select = {word: count for word, count in self.words_count.items() + if count >= self.freq_min and " " not in word + and 1 < len(word) <= self.len_max + } + # 计算凝固度, 左右两边 + self.right_entropy = {} + self.left_entropy = {} + self.calculate_entropy(boundary_type="left") + self.calculate_entropy(boundary_type="right") + # self.words_count.clear() # 清除变量 + + def compute_aggregation(self): + """ + 计算凝固度 + :return: None + """ + self.aggregation = {} + for word, value in self.words_select.items(): + len_word = len(word) + score_aggs = [] + for i in range(1, len_word): # 候选词的左右两边各取一个字 + word_right = word[i:] + word_left = word[:i] + value_right = self.words_select.get(word_right, self.freq_min) + value_left = self.words_select.get(word_left, self.freq_min) + # score_agg_single = math.log(value) - math.log(value_right * value_left) + score_agg_single = value / (value_right * value_left) + # score_agg_single = math.log10(value) - math.log10(self.total_words) -math.log10((value_right * value_left)) + score_aggs.append(score_agg_single) + self.aggregation[word] = min(score_aggs) + + def find_word(self, text, use_type="text", freq_min=2, len_max=7, entropy_min=1.2, aggregation_min=0.5, use_avg=False): + """ + 新词发现与策略 + :param text: str, path or doc, like "大漠帝国。" or "/home/data/doc.txt" + :param use_type: str, "text" or "file", file of "utf-8" of "txt" + :param freq_min: int, 最小词频, 大于1 + :param len_max: int, 最大成词长度, 一般为5, 6, 7 + :param entropy_min: int, 最小词频, 大于1 + :param aggregation_min: int, 最大成词长度, 一般为5, 6, 7 + :return: + """ + self.aggregation_min = aggregation_min + self.entropy_min = entropy_min + self.freq_min = freq_min + self.len_max = len_max + self.count_word(text=text, use_type=use_type) + self.compute_entropys() + self.compute_aggregation() + self.new_words = {} + # 输出 + for word,value in self.words_select.items(): + if not use_avg and self.aggregation[word] > self.aggregation_min \ + and self.right_entropy[word] > self.entropy_min and self.left_entropy[word] > self.entropy_min: + self.new_words[word] = {} + # {"aggregation":"agg", "right_entropy":"r", "left_entropy":"l", "frequency":"f", "score":"s"} + self.new_words[word]["a"] = self.aggregation[word] # math.log10(self.aggregation[word]) - math.log10(self.total_words) + self.new_words[word]["r"] = self.right_entropy[word] + self.new_words[word]["l"] = self.left_entropy[word] + self.new_words[word]["f"] = value / self.total_words + self.new_words[word]["s"] = self.new_words[word]["f"] * self.new_words[word]["a"] * \ + (self.right_entropy[word] + self.left_entropy[word]) + elif use_avg and self.aggregation[word] > self.aggregation_min \ + and (self.right_entropy[word] + self.left_entropy[word]) > 2 * self.entropy_min: + self.new_words[word] = {} + # {"aggregation":"agg", "right_entropy":"r", "left_entropy":"l", "frequency":"f", "score":"s"} + self.new_words[word]["a"] = self.aggregation[word] + self.new_words[word]["r"] = self.right_entropy[word] + self.new_words[word]["l"] = self.left_entropy[word] + self.new_words[word]["f"] = value / self.total_words + self.new_words[word]["s"] = self.new_words[word]["f"] * self.new_words[word]["a"] * \ + (self.right_entropy[word] + self.left_entropy[word]) + + return self.new_words + + +if __name__ == '__main__': + text = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + # wc = count_word(text) + # path = "data/poet_tangsong.csv" + # wd = WordDiscovery() + # res = wd.find_word(text=path, use_type="file", freq_min=2, len_max=6, entropy_min=1.2, aggregation_min=0.4) + # from macropodus.preprocess.tools_common import txt_write + # import json + # res_s = json.dumps(res) + # txt_write([res_s], "res_s.txt") + # print(res) + # with open("res_s.txt", "r", encoding="utf-8") as fd: + # ff = fd.readlines()[0] + # res_ = json.loads(ff) + # res_soft = sorted(res_.items(), key=lambda d: d[1]['score'], reverse=True) + wd = WordDiscovery() + res = wd.find_word(text=text, use_type="text", use_avg=True, freq_min=2, len_max=7, entropy_min=0.4, aggregation_min=1.2) + for k, v in res.items(): + print(k, v) + while True: + print("请输入:") + ques = input() + res = wd.find_word(text=ques, use_type="text", use_avg=True, freq_min=2, len_max=7, entropy_min=0.52, aggregation_min=1.2) + for k, v in res.items(): + print(k, v) + # gg = 0 diff --git a/macropodus/similarity/__init__.py b/macropodus/similarity/__init__.py new file mode 100644 index 0000000..17042a2 --- /dev/null +++ b/macropodus/similarity/__init__.py @@ -0,0 +1,14 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 22:04 +# @author : Mo +# @function: + + +from macropodus.similarity.similarity_word2vec_char import SimW2vChar + + +# 文本相似度 +use_cache = True # 使用缓存 +swc = SimW2vChar(use_cache) +sim = swc.similarity diff --git a/macropodus/similarity/similarity_word2vec_char.py b/macropodus/similarity/similarity_word2vec_char.py new file mode 100644 index 0000000..072f64a --- /dev/null +++ b/macropodus/similarity/similarity_word2vec_char.py @@ -0,0 +1,69 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/17 14:50 +# @author : Mo +# @function: similarity of sentence of word2vec + + +from macropodus.base.word2vec import W2v + + +class SimW2vChar(W2v): + def __init__(self, use_cache): + super().__init__(use_cache) + + def encode(self, sent, type_encode="other"): + """ + 生成句向量, 字符级别, char + :param sent: str, like "大漠帝国" + :param type_encode: str, like "avg", "other" + :return: vector + """ + sentence_vec = self.w2v_char.wv[self.w2v_char.index2word[1]] * 0 + len_sent = len(sent) + for i in range(len_sent): + word = sent[i] + try: + sentence_vec = sentence_vec + self.w2v_char.wv[word] + except Exception as e: + sentence_vec = sentence_vec + 0.01 # unknow_know词加1 + if type_encode == "avg": + sentence_vec = sentence_vec / len_sent + return sentence_vec + + def similarity(self, sent1, sent2, type_sim="total", type_encode="avg"): + """ + 相似度计算, 默认余弦相似度+jaccard相似度 + :param sen1: str, like "大漠帝国" + :param sen2: str, like "Macropodus" + :param type_sim: str, like "total" or "cosine" + :param type_encode: str, like "other" or "avg" + :return: float, like 0.998 + """ + if sent1 and sent2: + encode_sen1 = self.encode(sent1, type_encode) + encode_sen2 = self.encode(sent2, type_encode) + score_res = self.cosine(encode_sen1, encode_sen2) + else: + score_res = 0.0 + if type_sim=="total": + score_jaccard = self.jaccard(sent1, sent2) + score_res = (score_res + score_jaccard)/2 + return score_res + + +if __name__ == '__main__': + + sent1 = "大漠帝国" + sent2 = "macropodus" + swc = SimW2vChar(use_cache=True) + sen_encede = swc.encode(sent1) + score = swc.similarity(sent1, sent2) + print(score) + gg = 0 + while True: + print("请输入sent1:") + sent1 = input() + print("请输入sent2:") + sent2 = input() + print(swc.similarity(sent1, sent2)) diff --git a/macropodus/summarize/__init__.py b/macropodus/summarize/__init__.py new file mode 100644 index 0000000..b51cb1b --- /dev/null +++ b/macropodus/summarize/__init__.py @@ -0,0 +1,86 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/18 22:10 +# @author : Mo +# @function: text summarize + + +# text_summarize of extractive +from macropodus.summarize.feature_base.word_significance import WordSignificanceSum +from macropodus.summarize.feature_base.text_pronouns import TextPronounsSum +from macropodus.summarize.graph_base.textrank import TextRankSum, TextRankKey +from macropodus.summarize.feature_base.text_teaser import TextTeaserSum +from macropodus.summarize.feature_base.mmr import MMRSum + +from macropodus.summarize.topic_base.topic_lda import LDASum +from macropodus.summarize.topic_base.topic_lsi import LSISum +from macropodus.summarize.topic_base.topic_nmf import NMFSum + +from macropodus.summarize.nous_base.lead_3 import Lead3Sum + + +# feature +wss = WordSignificanceSum() +tps = TextPronounsSum() +tts = TextTeaserSum() +mms = MMRSum() + +# graph-3 +trs = TextRankSum() +trk = TextRankKey() + +# nous +l3s = Lead3Sum() + +# topic +lds = LDASum() +lss = LSISum() +nms = NMFSum() + +# summarization +text_pronouns = tps.summarize +text_teaser = tts.summarize +word_sign = wss.summarize +textrank = trs.summarize +lead3 = l3s.summarize +mmr = mms.summarize +lda = lds.summarize +lsi = lss.summarize +nmf = nms.summarize + +# keyword +keyword = trk.keyword + + +def summarization(text, num=320, type_summarize="lda", topic_min=6, judge_topic=False, alpha=0.6, type_l='mix', model_type="textrank_sklearn", title=None): + """ + 文本摘要汇总 + :param text: str, like "你是。大漠帝国。不是吧错了。哈哈。我的。" + :param num: int, like 32 + :param type_summarize: str, like "lda", must in ['text_pronouns', 'text_teaser', 'word_sign', 'textrank', 'lead3', 'mmr', 'lda', 'lsi', 'nmf'] + :return: + """ + + if type_summarize=="text_pronouns": # title, str, 可填标题, like "震惊,MacropodusXXX" + res = text_pronouns(text, num, title) + elif type_summarize=="text_teaser": # title, str, 可填标题, like "震惊,MacropodusXXX" + res = text_teaser(text, num, title) + elif type_summarize=="word_sign": # + res = word_sign(text, num) + elif type_summarize=="textrank": # model_type 可填 'textrank_textrank4zh', 'text_rank_sklearn' or 'textrank_gensim' + res = textrank(text, num) + elif type_summarize=="lead3": + res = lead3(text, num, type_l) # type_l 可填 'begin', 'end' or 'mix' + elif type_summarize=="mmr": + res = mmr(text, num, alpha) # alpha 可填 0-1 + elif type_summarize=="lda": # topic_min>1, judge_topic=True or False + res = lda(text, num, topic_min, judge_topic) + elif type_summarize=="lsi": # topic_min>1, judge_topic=True or False + res = lsi(text, num, topic_min, judge_topic) + elif type_summarize=="nmf": # topic_min>1, judge_topic=True or False + res = nmf(text, num, topic_min, judge_topic) + else: + raise RuntimeError("your input type_summarize is wrong, it must be in " + "['text_pronouns', 'text_teaser', 'word_sign', " + "'textrank', 'lead3', 'mmr', 'lda', 'lsi', 'nmf']") + return res diff --git a/macropodus/summarize/feature_base/__init__.py b/macropodus/summarize/feature_base/__init__.py new file mode 100644 index 0000000..5ac897f --- /dev/null +++ b/macropodus/summarize/feature_base/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/25 21:41 +# @author :Mo +# @function : \ No newline at end of file diff --git a/macropodus/summarize/feature_base/mmr.py b/macropodus/summarize/feature_base/mmr.py new file mode 100644 index 0000000..af7ee07 --- /dev/null +++ b/macropodus/summarize/feature_base/mmr.py @@ -0,0 +1,90 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/28 20:16 +# @author :Mo +# @function :mmr + + +from macropodus.preprocess.tools_ml import extract_chinese, cut_sentence +from macropodus.preprocess.tools_ml import macropodus_cut, tfidf_fit +from macropodus.data.words_common.stop_words import stop_words +import copy + + +class MMRSum: + def __init__(self): + self.stop_words = stop_words.values() + self.algorithm = 'mmr' + + def summarize(self, text, num=8, alpha=0.6): + """ + + :param text: str + :param num: int + :return: list + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + self.sentences_cut = [" ".join(sc) for sc in self.sentences_cut] + # # 计算每个句子的词语个数 + # sen_word_len = [len(sc)+1 for sc in sentences_cut] + # 计算每个句子的tfidf + sen_tfidf = tfidf_fit(self.sentences_cut) + # 矩阵中两两句子相似度 + SimMatrix = (sen_tfidf * sen_tfidf.T).A # 例如: SimMatrix[1, 3] # "第2篇与第4篇的相似度" + # 输入文本句子长度 + len_sen = len(self.sentences) + # 句子标号 + sen_idx = [i for i in range(len_sen)] + summary_set = [] + mmr = {} + for i in range(len_sen): + if not self.sentences[i] in summary_set: + sen_idx_pop = copy.deepcopy(sen_idx) + sen_idx_pop.pop(i) + # 两两句子相似度 + sim_i_j = [SimMatrix[i, j] for j in sen_idx_pop] + score_tfidf = sen_tfidf[i].toarray()[0].sum() # / sen_word_len[i], 如果除以词语个数就不准确 + mmr[self.sentences[i]] = alpha * score_tfidf - (1 - alpha) * max(sim_i_j) + summary_set.append(self.sentences[i]) + score_sen = [(rc[1], rc[0]) for rc in sorted(mmr.items(), key=lambda d: d[1], reverse=True)] + if len(mmr) > num: + score_sen = score_sen[0:num] + return score_sen + + +if __name__ == '__main__': + mmr_sum = MMRSum() + doc = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + sum = mmr_sum.summarize(doc) + for i in sum: + print(i) + + + + + + diff --git a/macropodus/summarize/feature_base/text_pronouns.py b/macropodus/summarize/feature_base/text_pronouns.py new file mode 100644 index 0000000..d7b6426 --- /dev/null +++ b/macropodus/summarize/feature_base/text_pronouns.py @@ -0,0 +1,235 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/8/25 20:51 +# @author :Mo +# @paper :Sentence Extraction Based Single Document Summarization(2005) +# @function :text summary of feature-base +# @evaluate :bad, it is for english, and that's not clearly explain of formula + + +from macropodus.preprocess.tools_ml import macropodus_cut, jieba_tag_cut +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import extract_chinese +from macropodus.preprocess.tools_ml import cut_sentence +from macropodus.preprocess.tools_ml import get_ngrams +# import jieba.analyse as analyse +from collections import Counter + + +# # jieba预训练好的idf值 +# default_tfidf = analyse.default_tfidf +# # 引入TF-IDF关键词抽取接口 +# tfidf = analyse.extract_tags +# # 引入TextRank关键词抽取接口 +# textrank = analyse.textrank + + +CHAR_PUMCTUATION = ',.:;?!`\'"[]{}<>。？！，、；：“” ‘’「」『』《》（）[]〔〕【】——……—-～·《》〈〉﹏﹏.___' +CHAR_ENGLISH = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +CHAR_NUMBER = "0123456789零一二两三四五六七八九" +CHAR_CHINESE = "\u4e00-\u9fa5" +ES_MIN = 1e-9 + + +class TextPronounsSum: + def __init__(self): + self.algorithm = 'text_pronouns' + self.stop_words = stop_words.values() + self.len_ideal = 18 # 中心句子长度, 默认 + + def score_position(self): + """ + 文本长度得分 + :param sentence: + :return: + """ + score_position = [] + for i, _ in enumerate(self.sentences): + score_standard = i / (len(self.sentences)) + if score_standard >= 0 and score_standard <= 0.1: + score_position.append(0.17) + elif score_standard > 0.1 and score_standard <= 0.2: + score_position.append(0.23) + elif score_standard > 0.2 and score_standard <= 0.3: + score_position.append(0.14) + elif score_standard > 0.3 and score_standard <= 0.4: + score_position.append(0.08) + elif score_standard > 0.4 and score_standard <= 0.5: + score_position.append(0.05) + elif score_standard > 0.5 and score_standard <= 0.6: + score_position.append(0.04) + elif score_standard > 0.6 and score_standard <= 0.7: + score_position.append(0.06) + elif score_standard > 0.7 and score_standard <= 0.8: + score_position.append(0.04) + elif score_standard > 0.8 and score_standard <= 0.9: + score_position.append(0.04) + elif score_standard > 0.9 and score_standard <= 1.0: + score_position.append(0.15) + else: + score_position.append(0) + return score_position + + def score_length(self): + """ + 文本长度得分 + :param sentence: + :return: + """ + score_length = [] + for i, sentence in enumerate(self.sentences): + score_len = 1 - abs(self.len_ideal - len(sentence)) / self.len_ideal + score_length.append(score_len) + return score_length + + def score_tag(self): + """ + 词性打分名词-动词-代词(n,v,r) + :return: + """ + score_tag = [] + for i, sen_tag_score in enumerate(self.sentences_tag_cut): + sen_tag = sen_tag_score.values() + tag_dict = dict(Counter(sen_tag)) + tag_n = tag_dict.get('n', 0) + tag_dict.get('nr', 0) + tag_dict.get('ns', 0) + \ + tag_dict.get('nt', 0) + tag_dict.get('nz', 0) + tag_dict.get('ng', 0) + tag_v = tag_dict.get('v', 0) + tag_dict.get('vd', 0) + tag_dict.get('vn', 0) + tag_dict.get('vg', 0) + tag_p = tag_dict.get('r', 0) + score_sen_tag = (1.2 * tag_n + 1.0 * tag_v + 0.8 * tag_p)/(len(sen_tag_score) + 1) + score_tag.append(score_sen_tag) + return score_tag + + def score_title(self, words): + """ + 与标题重合部分词语 + :param words: + :return: + """ + mix_word = [word for word in words if word in self.title] + len_mix_word = len(mix_word) + len_title_word = len(self.title) + return (len_mix_word + 1.0) / (len_mix_word + 2.0) / len_title_word + + def summarize(self, text, num=320, title=None): + """ + 文本句子排序 + :param docs: list + :return: list + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + self.title = title + if self.title: + self.title = macropodus_cut(title) + # 切词,含词性标注 + self.sentences_tag_cut = [jieba_tag_cut(extract_chinese(sentence)) for sentence in self.sentences] + # 词语,不含词性标注 + sentences_cut = [[jc for jc in jtc.keys() ] for jtc in self.sentences_tag_cut] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + # 词频统计 + self.words = [] + for sen in self.sentences_cut: + self.words = self.words + sen + self.word_count = dict(Counter(self.words)) + # 按频次计算词语的得分, 得到self.word_freq=[{'word':, 'freq':, 'score':}] + self.word_freqs = {} + self.len_words = len(self.words) + for k, v in self.word_count.items(): + self.word_freqs[k] = v * 0.5 / self.len_words + # uni_bi_tri_gram特征 + [gram_uni, gram_bi, gram_tri] = get_ngrams("".join(self.sentences), ns=[1, 2, 3]) + ngrams = gram_uni + gram_bi + gram_tri + self.ngrams_count = dict(Counter(ngrams)) + # 句子位置打分 + scores_posi = self.score_position() + # 句子长度打分 + scores_length = self.score_length() + # 句子词性打分, 名词(1.2)-代词(0.8)-动词(1.0) + scores_tag = self.score_tag() + + res_rank = {} + self.res_score = [] + for i in range(len(sentences_cut)): + sen_cut = self.sentences_cut[i] # 句子中的词语 + # ngram得分 + [gram_uni_, gram_bi_, gram_tri_] = get_ngrams(self.sentences[i], ns=[1, 2, 3]) # gram_uni_bi_tri(self.sentences[i]) + n_gram_s = gram_uni_ + gram_bi_ + gram_tri_ + score_ngram = sum([self.ngrams_count[ngs] if ngs in self.ngrams_count else 0 for ngs in n_gram_s]) / (len(n_gram_s) + 1) + # 句子中词语的平均长度 + score_word_length_avg = sum([len(sc) for sc in sen_cut])/(len(sen_cut)+1) + score_posi = scores_posi[i] + score_length = scores_length[i] + score_tag = scores_tag[i] + if self.title: # 有标题的文本打分合并 + score_title = self.score_title(sen_cut) + score_total = (score_title * 0.5 + score_ngram * 2.0 + score_word_length_avg * 0.5 + + score_length * 0.5 + score_posi * 1.0 + score_tag * 0.6) / 6.0 + # 可查阅各部分得分统计 + self.res_score.append(["score_title", "score_ngram", "score_word_length_avg", + "score_length", "score_posi", "score_tag"]) + self.res_score.append([score_title, score_ngram, score_word_length_avg, + score_length, score_posi, score_tag, self.sentences[i]]) + else: # 无标题的文本打分合并 + score_total = (score_ngram * 2.0 + score_word_length_avg * 0.5 + score_length * 0.5 + + score_posi * 1.0 + score_tag * 0.6) / 5.0 + # 可查阅各部分得分统计 + self.res_score.append(["score_ngram", "score_word_length_avg", + "score_length", "score_posi", "score_tag"]) + self.res_score.append([score_ngram, score_word_length_avg, + score_length, score_posi, score_tag, self.sentences[i]]) + res_rank[self.sentences[i].strip()] = score_total + # 最小句子数 + num_min = min(num, int(len(self.word_count) * 0.6)) + res_rank_sort = sorted(res_rank.items(), key=lambda rr: rr[1], reverse=True) + res_rank_sort_reverse = [(rrs[1], rrs[0]) for rrs in res_rank_sort][0:num_min] + return res_rank_sort_reverse + + + +if __name__ == '__main__': + sen = "自然语言理解（NLU，Natural Language Understanding）: 使计算机理解自然语言（人类语言文字）等，重在理解。" + tp = TextPronounsSum() + docs ="和投票目标的等级来决定新的等级.简单的说。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。" \ + "业界急需一种相对比较准确的网页重要性计算方法。" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。" + + docs1 = "/article/details/98530760。" \ + "CSDN\n。" \ + "文本生成NLG，不同于文本理解NLU（例如分词、词向量、分类、实体提取。" \ + "是重在文本生成的另一种关键技术（常用的有翻译、摘要、同义句生成等）。" \ + "传统的文本生成NLG任务主要是抽取式的，生成式的方法看起来到现在使用也没有那么普遍。" \ + "现在，我记录的是textrank，一种使用比较广泛的抽取式关键句提取算法。" \ + "版权声明：本文为CSDN博主「大漠帝国」的原创文章，遵循CC 4.0 by-sa版权协议，" \ + "转载请附上原文出处链接及本声明。原文链接：https://blog.csdn.net/rensihui" \ + "CSDN是神" + sums = tp.summarize(docs) + for sum_ in sums: + print(sum_) + + # ran_20 = range(20) + # print(type(ran_20)) + # print(ran_20) + # idx = [1,2,3] + # idx.pop(1) + # print(idx) + # print(max([1,2,3,4])) + + + + diff --git a/macropodus/summarize/feature_base/text_teaser.py b/macropodus/summarize/feature_base/text_teaser.py new file mode 100644 index 0000000..dc5b51b --- /dev/null +++ b/macropodus/summarize/feature_base/text_teaser.py @@ -0,0 +1,193 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/26 20:02 +# @author :Mo +# @function :text summary of feature-base of TextTeaser +# @paper :Automatic Text Summarization for Indonesian Language Using TextTeaser(2013) +# @url :using Google Scholar + + +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import extract_chinese +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence +from collections import Counter + + +class TextTeaserSum: + def __init__(self): + self.algorithm = 'text_teaser' + self.stop_words = stop_words.values() + self.len_ideal = 18 # 中心句子长度, 默认 + + def score_position(self): + """ + 文本句子位置得分 + :param sentence: + :return: + """ + score_position = [] + for i, sen in enumerate(self.sentences): + score_standard = i / (len(self.sentences)) + if score_standard >= 0 and score_standard <= 0.1: + score_position.append(0.17) + elif score_standard > 0.1 and score_standard <= 0.2: + score_position.append(0.23) + elif score_standard > 0.2 and score_standard <= 0.3: + score_position.append(0.14) + elif score_standard > 0.3 and score_standard <= 0.4: + score_position.append(0.08) + elif score_standard > 0.4 and score_standard <= 0.5: + score_position.append(0.05) + elif score_standard > 0.5 and score_standard <= 0.6: + score_position.append(0.04) + elif score_standard > 0.6 and score_standard <= 0.7: + score_position.append(0.06) + elif score_standard > 0.7 and score_standard <= 0.8: + score_position.append(0.04) + elif score_standard > 0.8 and score_standard <= 0.9: + score_position.append(0.04) + elif score_standard > 0.9 and score_standard <= 1.0: + score_position.append(0.15) + else: + score_position.append(0) + return score_position + + def score_length(self, sentence): + """ + 文本长度得分 + :param sentence: + :return: + """ + score_length = 1 - min(abs(self.len_ideal - len(sentence)), self.len_ideal) / self.len_ideal + return score_length + + def score_sbs(self, words): + """ + 单个句子的sbs分数 + :param words: + :return: + """ + score_sbs = 0.0 + for word in words: + if word in self.word_freqs: + score_sbs += self.word_freqs[word] + return ((1.0 / abs(len(words))) if len(words) else 1e-9) * score_sbs + + def score_dbs(self, words): + """ + 单个句子的dbs分数 + :param words: + :return: + """ + words_all = list(self.word_freqs.keys()) + pun = len(set(words)&set(words_all)) + 1 + score_dbs = 0.0 + wf_first = [] + for i, word in enumerate(words): + if word in words_all: + index = words_all.index(word) + if not wf_first: + wf_first = [index, self.word_freqs[word]] + else: + score_dbs += wf_first[1]*self.word_freqs[word] / (((wf_first[0] - index) if (wf_first[0] - index)!=0 else self.len_words)**2) + score_dbs = score_dbs if score_dbs !=0 else 1e-9 + return (1.0 / pun * (pun + 1.0)) * score_dbs + + def score_title(self, words): + """ + 与标题重合部分词语 + :param words: + :return: + """ + mix_word = [word for word in words if word in self.title] + len_mix_word = len(mix_word) + len_title_word = len(self.title) + return (len_mix_word + 1.0) / (len_mix_word + 2.0) / len_title_word + + def summarize(self, text, num=320, title=None): + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + self.title = title + if self.title: + self.title = macropodus_cut(title) + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + # 词频统计 + self.words = [] + for sen in self.sentences_cut: + self.words = self.words + sen + self.word_count = dict(Counter(self.words)) + # word_count_rank = sorted(word_count.items(), key=lambda f:f[1], reverse=True) + # self.word_freqs = [{'word':wcr[0], 'freq':wcr[1]} for wcr in word_count_rank] + # 按频次计算词语的得分, 得到self.word_freq=[{'word':, 'freq':, 'score':}] + self.word_freqs = {} + self.len_words = len(self.words) + for k, v in self.word_count.items(): + self.word_freqs[k] = v * 0.5 / self.len_words + # 句子位置打分 + scores_posi = self.score_position() + res_rank = {} + self.res_score = [] + for i in range(len(sentences_cut)): + sen = self.sentences[i] # 句子 + sen_cut = self.sentences_cut[i] # 句子中的词语 + score_sbs = self.score_sbs(sen_cut) # 句子中的词语打分1 + score_dbs = self.score_dbs(sen_cut) # 句子中的词语打分2 + score_word = (score_sbs + score_dbs) * 10.0 / 2.0 # 句子中的词语打分mix + score_length = self.score_length(sen) # 句子文本长度打分 + score_posi = scores_posi[i] + if self.title: # 有标题的文本打分合并 + score_title = self.score_title(sen_cut) + score_total = (score_title * 0.5 + score_word * 2.0 + score_length * 0.5 + score_posi * 1.0) / 4.0 + # 可查阅各部分得分统计 + self.res_score.append(["score_total", "score_sbs", "score_dbs", "score_word", "score_length", "score_posi", "score_title", "sentences"]) + self.res_score.append([score_total, score_sbs, score_dbs, score_word, score_length, score_posi, score_title, self.sentences[i]]) + else: # 无标题的文本打分合并 + score_total = (score_word * 2.0 + score_length * 0.5 + score_posi * 1.0) / 3.5 + self.res_score.append(["score_total", "score_sbs", "score_dbs", "score_word", "score_length", "score_posi", "sentences"]) + self.res_score.append([score_total, score_sbs, score_dbs, score_word, score_length, score_posi, self.sentences[i].strip()]) + res_rank[self.sentences[i].strip()] = score_total + # 最小句子数 + num_min = min(num, int(len(self.word_count) * 0.6)) + score_sen = [(rc[1], rc[0]) for rc in sorted(res_rank.items(), key=lambda d: d[1], reverse=True)][0:num_min] + return score_sen + + +if __name__ == '__main__': + doc1 = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + title = "方直科技等公司合伙设立教育投资基金" + doc = "多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，" \ + "与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。" \ + "该基金认缴出资总规模为人民币3.01亿元。" \ + "基金的出资方式具体如下：出资进度方面，基金合伙人的出资应于基金成立之日起四年内分四期缴足，每期缴付7525万元；" \ + "各基金合伙人每期按其出资比例缴付。合伙期限为11年，投资目标为教育领域初创期或成长期企业。" \ + "截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:" \ + "公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。" \ + "方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + tt = TextTeaserSum() + res_ = tt.summarize(doc) + for res in res_: + print(res) + gg = 0 diff --git a/macropodus/summarize/feature_base/word_significance.py b/macropodus/summarize/feature_base/word_significance.py new file mode 100644 index 0000000..54ee7b7 --- /dev/null +++ b/macropodus/summarize/feature_base/word_significance.py @@ -0,0 +1,112 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/8/26 23:42 +# @author :Mo +# @function :text summarize of extraction of word significance +# @paper :The Automatic Creation of Literature Abstracts* +# @url :http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.pdf + + +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import extract_chinese +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence +from collections import Counter + + +class WordSignificanceSum: + def __init__(self): + """ + features: + 1. words mix in title and sentence + 2. keywords in sentence + 3. Position of sentence + 4. Length of sentence + """ + self.algorithm = 'word_significance' + self.stop_words = stop_words.values() + self.num = 0 + + def summarize(self, text, num=320): + """ + 根据词语意义确定中心句 + :param text: str + :param num: int + :return: list + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + # 词频统计 + self.words = [] + for sen in self.sentences_cut: + self.words = self.words + sen + self.word_count = dict(Counter(self.words)) + self.word_count_rank = sorted(self.word_count.items(), key=lambda f: f[1], reverse=True) + # 最小句子数 + num_min = min(num, int(len(self.word_count)*0.6)) + # 词语排序, 按照词频 + self.word_rank = [wcr[0] for wcr in self.word_count_rank][0:num_min] + res_sentence = [] + # 抽取句子, 顺序, 如果词频高的词语在句子里, 则抽取 + for word in self.word_rank: + for i in range(0, len(self.sentences)): + # 当返回关键句子到达一定量, 则结束返回 + if len(res_sentence) < num_min: + added = False + for sent in res_sentence: + if sent == self.sentences[i]: added = True + if (added == False and word in self.sentences[i]): + res_sentence.append(self.sentences[i]) + break + # 只是计算各得分,没什么用 + len_sentence = len(self.sentences) + res_sentence = [(1-1/(len_sentence+len_sentence/(k+1)), rs) for k, rs in enumerate(res_sentence)] + return res_sentence + + +if __name__ == "__main__": + doc = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + + doc1 = "多知网. "\ + "多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，" \ + "与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。" \ + "该基金认缴出资总规模为人民币3.01亿元。" \ + "基金的出资方式具体如下：出资进度方面，基金合伙人的出资应于基金成立之日起四年内分四期缴足，每期缴付7525万元；" \ + "各基金合伙人每期按其出资比例缴付。合伙期限为11年，投资目标为教育领域初创期或成长期企业。" \ + "截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:" \ + "公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。" \ + "方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + + ws = WordSignificanceSum() + res = ws.summarize(doc, num=6) + for r in res: + print(r) + + +"多知网. 多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。该基金认缴出资总规模为人民币3.01亿元。基金的出资方式具体如下：出资进度方面，基金合伙人的出资" \ +"应于基金成立之日起四年内分四期缴足，每期缴付7525万元；各基金合伙人每期按其出资比例缴付。" \ +"合伙期限为11年，投资目标为教育领域初创期或成长期企业。截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + diff --git a/macropodus/summarize/graph_base/__init__.py b/macropodus/summarize/graph_base/__init__.py new file mode 100644 index 0000000..fa6627c --- /dev/null +++ b/macropodus/summarize/graph_base/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/25 21:42 +# @author :Mo +# @function : \ No newline at end of file diff --git a/macropodus/summarize/graph_base/textrank.py b/macropodus/summarize/graph_base/textrank.py new file mode 100644 index 0000000..7ea7cd9 --- /dev/null +++ b/macropodus/summarize/graph_base/textrank.py @@ -0,0 +1,84 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/29 22:39 +# @author :Mo +# @function :textrank of textrank4zh, sklearn or gensim + + +from macropodus.summarize.graph_base.textrank_word2vec import TextrankWord2vec +from macropodus.summarize.graph_base.textrank_gensim import TextrankGensimSum +from macropodus.summarize.graph_base.textrank_sklearn import TextrankSklearn + + +# textrank of gensim +trgs = TextrankGensimSum() +# textrank of word2vec +trwv = TextrankWord2vec() +# textrank of sklearn +trsk = TextrankSklearn() + + +class TextRankSum: + def __init__(self): + self.algorithm = 'textrank' + + def summarize(self, text, num=6, model_type="textrank_word2vec"): + """ + 文本摘要 + :param text:str, like "你好！大漠帝国！" + :param num: int, like 3 + :param model_type: str, like "textrank_sklearn" + :return: list + """ + if model_type=="textrank_sklearn": + res = trsk.summarize(text, num=num) + elif model_type=="textrank_gensim": + res = trgs.summarize(text, num=num) + elif model_type=="textrank_word2vec": + res = trwv.summarize(text, num=num) + else: + raise RuntimeError(" model_type must be 'textrank_textrank4zh', 'text_rank_sklearn' or 'textrank_gensim' ") + + return res + +class TextRankKey: + def __init__(self): + self.algorithm = 'keyword' + + def keyword(self, text, num=6, score_min=0.025, model_type="keywor_word2vec"): + if model_type=="keywor_word2vec": + res = trwv.keyword(text, num=num, score_min=score_min) + else: + raise RuntimeError(" model_type must be 'keywor_word2vec'") + + return res + + + +if __name__ == '__main__': + + doc = "和投票目标的等级来决定新的等级.简单的说。" \ + "是上世纪90年代末提出的一种计算网页权重的算法!" \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。" \ + "业界急需一种相对比较准确的网页重要性计算方法。" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。" + + text = doc.encode('utf-8').decode('utf-8') + + tr = TextRankSum() + kw = TextRankKey() + score_ques = tr.summarize(text, num=100, model_type="textrank_gensim") # "text_rank_sklearn") + for sq in score_ques: + print(sq) + + score_ques = kw.keyword(text, num=100, model_type="keywor_word2vec") # "text_rank_sklearn") + for sq in score_ques: + print(sq) diff --git a/macropodus/summarize/graph_base/textrank_gensim.py b/macropodus/summarize/graph_base/textrank_gensim.py new file mode 100644 index 0000000..48a2b63 --- /dev/null +++ b/macropodus/summarize/graph_base/textrank_gensim.py @@ -0,0 +1,362 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/8/21 22:01 +# @author :Mo +# @function :textrank using gensim summarization of chinese. (split is '. ', '! ', '. ' and so on) +# @code from:most code from https://github.com/RaRe-Technologies/gensim + +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html +# """This module provides functions for summarizing texts. Summarizing is based on +# ranks of text sentences using a variation of the TextRank algorithm [1]_. +# +# .. [1] Federico Barrios, Federico L´opez, Luis Argerich, Rosita Wachenchauzer (2016). +# Variations of the Similarity Function of TextRank for Automated Summarization, +# https://arxiv.org/abs/1602.03606 + + +from gensim.summarization.commons import remove_unreachable_nodes as _remove_unreachable_nodes +from gensim.summarization.pagerank_weighted import pagerank_weighted as _pagerank +from gensim.summarization.commons import build_graph as _build_graph +from gensim.summarization.bm25 import iter_bm25_bow as _bm25_weights +from gensim.corpora import Dictionary +from gensim.utils import deprecated +from math import log10 as _log10 +from six.moves import range +import logging + +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence + + +logger = logging.getLogger(__name__) +WEIGHT_THRESHOLD = 1.e-3 +INPUT_MIN_LENGTH = 2 + + +class TextrankGensimSum: + def __init__(self): + self.algorithm = 'textrank_gensim' + self.stop_words = stop_words.values() + self.len_ideal = 18 # 中心句子长度, 默认 + + def summarize(self, text, num=320): + # 切句 + if type(text) == str: + sentences = cut_sentence(text) + elif type(text) == list: + sentences = text + else: + raise RuntimeError("text type must be list or str") + # str of sentence >>> index + corpus = _build_corpus(sentences) + # pagerank and so on + most_important_docs = summarize_corpus(corpus) + + count = 0 + sentences_score = {} + for cor in corpus: + tuple_cor = tuple(cor) + sentences_score[sentences[count]] = most_important_docs[tuple_cor] + count += 1 + # 最小句子数 + num_min = min(num, int(len(sentences) * 0.6)) + score_sen = [(rc[1], rc[0]) for rc in sorted(sentences_score.items(), + key=lambda d: d[1], reverse=True)][0:num_min] + return score_sen + + + +def _set_graph_edge_weights(graph): + """Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small, + forces all weights to 1, inplace. + + Parameters + ---------- + graph : :class:`~gensim.summarization.graph.Graph` + Given graph. + + """ + documents = graph.nodes() + weights = _bm25_weights(documents) + + for i, doc_bow in enumerate(weights): + if i % 1000 == 0 and i > 0: + logger.info('PROGRESS: processing %s/%s doc (%s non zero elements)', i, len(documents), len(doc_bow)) + + for j, weight in doc_bow: + if i == j or weight < WEIGHT_THRESHOLD: + continue + + edge = (documents[i], documents[j]) + + if not graph.has_edge(edge): + graph.add_edge(edge, weight) + + # Handles the case in which all similarities are zero. + # The resultant summary will consist of random sentences. + if all(graph.edge_weight(edge) == 0 for edge in graph.iter_edges()): + _create_valid_graph(graph) + + +def _create_valid_graph(graph): + """Sets all weights of edges for different edges as 1, inplace. + + Parameters + ---------- + graph : :class:`~gensim.summarization.graph.Graph` + Given graph. + + """ + nodes = graph.nodes() + + for i in range(len(nodes)): + for j in range(len(nodes)): + if i == j: + continue + + edge = (nodes[i], nodes[j]) + + if graph.has_edge(edge): + graph.del_edge(edge) + + graph.add_edge(edge, 1) + + +@deprecated("Function will be removed in 4.0.0") +def _get_doc_length(doc): + """Get length of (tokenized) document. + + Parameters + ---------- + doc : list of (list of (tuple of int)) + Given document. + + Returns + ------- + int + Length of document. + + """ + return sum(item[1] for item in doc) + + +@deprecated("Function will be removed in 4.0.0") +def _get_similarity(doc1, doc2, vec1, vec2): + """Returns similarity of two documents. + + Parameters + ---------- + doc1 : list of (list of (tuple of int)) + First document. + doc2 : list of (list of (tuple of int)) + Second document. + vec1 : array + ? of first document. + vec1 : array + ? of secont document. + + Returns + ------- + float + Similarity of two documents. + + """ + numerator = vec1.dot(vec2.transpose()).toarray()[0][0] + length_1 = _get_doc_length(doc1) + length_2 = _get_doc_length(doc2) + + denominator = _log10(length_1) + _log10(length_2) if length_1 > 0 and length_2 > 0 else 0 + + return numerator / denominator if denominator != 0 else 0 + + +def _build_corpus(sentences): + """Construct corpus from provided sentences. + + Parameters + ---------- + sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Given sentences. + + Returns + ------- + list of list of (int, int) + Corpus built from sentences. + + """ + split_tokens = [macropodus_cut(sentence) for sentence in sentences] + dictionary = Dictionary(split_tokens) + return [dictionary.doc2bow(token) for token in split_tokens] + + +def _get_important_sentences(sentences, corpus, important_docs): + """Get most important sentences. + + Parameters + ---------- + sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Given sentences. + corpus : list of list of (int, int) + Provided corpus. + important_docs : list of list of (int, int) + Most important documents of the corpus. + + Returns + ------- + list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Most important sentences. + + """ + hashable_corpus = _build_hasheable_corpus(corpus) + sentences_by_corpus = dict(zip(hashable_corpus, sentences)) + return [sentences_by_corpus[tuple(important_doc)] for important_doc in important_docs] + + +def _get_sentences_with_word_count(sentences, word_count): + """Get list of sentences. Total number of returned words close to specified `word_count`. + + Parameters + ---------- + sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Given sentences. + word_count : int or None + Number of returned words. If None full most important sentences will be returned. + + Returns + ------- + list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Most important sentences. + + """ + length = 0 + selected_sentences = [] + + # Loops until the word count is reached. + for sentence in sentences: + words_in_sentence = len(sentence.text.split()) + + # Checks if the inclusion of the sentence gives a better approximation + # to the word parameter. + if abs(word_count - length - words_in_sentence) > abs(word_count - length): + return selected_sentences + + selected_sentences.append(sentence) + length += words_in_sentence + + return selected_sentences + + +def _extract_important_sentences(sentences, corpus, important_docs, word_count): + """Get most important sentences of the `corpus`. + + Parameters + ---------- + sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Given sentences. + corpus : list of list of (int, int) + Provided corpus. + important_docs : list of list of (int, int) + Most important docs of the corpus. + word_count : int + Number of returned words. If None full most important sentences will be returned. + + Returns + ------- + list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` + Most important sentences. + + """ + important_sentences = _get_important_sentences(sentences, corpus, important_docs) + + # If no "word_count" option is provided, the number of sentences is + # reduced by the provided ratio. Else, the ratio is ignored. + return important_sentences \ + if word_count is None \ + else _get_sentences_with_word_count(important_sentences, word_count) + + +def _format_results(extracted_sentences, split): + """Returns `extracted_sentences` in desired format. + + Parameters + ---------- + extracted_sentences : list of :class:~gensim.summarization.syntactic_unit.SyntacticUnit + Given sentences. + split : bool + If True sentences will be returned as list. Otherwise sentences will be merged and returned as string. + + Returns + ------- + list of str + If `split` **OR** + str + Formatted result. + + """ + if split: + return [sentence for sentence in extracted_sentences] + return "\n".join(sentence.text for sentence in extracted_sentences) + + +def _build_hasheable_corpus(corpus): + """Hashes and get `corpus`. + + Parameters + ---------- + corpus : list of list of (int, int) + Given corpus. + + Returns + ------- + list of list of (int, int) + Hashable corpus. + + """ + return [tuple(doc) for doc in corpus] + + +def summarize_corpus(corpus): + """Get a list of the most important documents of a corpus using a variation of the TextRank algorithm [1]_. + Used as helper for summarize :func:`~gensim.summarization.summarizer.summarizer` + + Note + ---- + The input must have at least :const:`~gensim.summarization.summarizer.INPUT_MIN_LENGTH` documents for the summary + to make sense. + + + Parameters + ---------- + corpus : list of list of (int, int) + Given corpus. + ratio : float, optional + Number between 0 and 1 that determines the proportion of the number of + sentences of the original text to be chosen for the summary, optional. + + Returns + ------- + list of str + Most important documents of given `corpus` sorted by the document score, highest first. + + """ + hashable_corpus = _build_hasheable_corpus(corpus) + + logger.info('Building graph') + graph = _build_graph(hashable_corpus) + + logger.info('Filling graph') + _set_graph_edge_weights(graph) + + logger.info('Removing unreachable nodes of graph') + _remove_unreachable_nodes(graph) + + logger.info('Pagerank graph') + pagerank_scores = _pagerank(graph) + return pagerank_scores + + # logger.info('Sorting pagerank scores') + # hashable_corpus.sort(key=lambda doc: pagerank_scores.get(doc, 0), reverse=True) + # + # return [list(doc) for doc in hashable_corpus] + diff --git a/macropodus/summarize/graph_base/textrank_sklearn.py b/macropodus/summarize/graph_base/textrank_sklearn.py new file mode 100644 index 0000000..d2a1447 --- /dev/null +++ b/macropodus/summarize/graph_base/textrank_sklearn.py @@ -0,0 +1,60 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/8/21 22:01 +# @author :Mo +# @function : textrank using tfidf of sklearn, pagerank of networkx + + +from sklearn.feature_extraction.text import TfidfTransformer +from macropodus.preprocess.tools_ml import cut_sentence +from macropodus.preprocess.tools_ml import tdidf_sim +import networkx as nx + + +class TextrankSklearn: + def __init__(self): + self.algorithm = 'textrank_sklearn' + + def summarize(self, text, num=320): + # 切句 + if type(text) == str: + sentences = cut_sentence(text) + elif type(text) == list: + sentences = text + else: + raise RuntimeError("text type must be list or str") + # tf-idf相似度 + matrix = tdidf_sim(sentences) + matrix_norm = TfidfTransformer().fit_transform(matrix) + # 构建相似度矩阵 + tfidf_sim = nx.from_scipy_sparse_matrix(matrix_norm * matrix_norm.T) + # nx.pagerank + sens_scores = nx.pagerank(tfidf_sim) + # 得分排序 + sen_rank = sorted(sens_scores.items(), key=lambda x: x[1], reverse=True) + # 保留topk个, 防止越界 + topk = min(len(sentences), num) + # 返回原句子和得分 + return [(sr[1], sentences[sr[0]]) for sr in sen_rank][0:topk] + + +if __name__ == '__main__': + doc = "是上世纪90年代末提出的一种计算网页权重的算法。" \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长，" \ + "业界急需一种相对比较准确的网页重要性计算方法，" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票，" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面" \ + "和投票目标的等级来决定新的等级。简单的说，" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "PageRank The PageRank Citation Ranking: Bringing Order to the Web，"\ + "具体说来就是，PageRank有两个基本思想，也可以说是假设，" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）；" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信息。" + doc = doc.encode('utf-8').decode('utf-8') + ts = TextrankSklearn() + textrank_tfidf = ts.summarize(doc, 32) + for score_sen in textrank_tfidf: + print(score_sen) diff --git a/macropodus/summarize/graph_base/textrank_word2vec.py b/macropodus/summarize/graph_base/textrank_word2vec.py new file mode 100644 index 0000000..48dc105 --- /dev/null +++ b/macropodus/summarize/graph_base/textrank_word2vec.py @@ -0,0 +1,156 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/20 20:39 +# @author :Mo +# @function :textrank of word2vec, keyword and sentence + +from macropodus.similarity.similarity_word2vec_char import SimW2vChar +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence +import networkx as nx +import numpy as np + + +class TextrankWord2vec(SimW2vChar): + def __init__(self, use_cache=True): + self.algorithm = 'textrank_word2vec' + self.stop_words = stop_words + super().__init__(use_cache) # self.w2v_char + + def cut_window(self, sent_words, win_size=2): + """ + 滑动窗口切词 + :param sent_words: list, like ["我", "是", "大漠帝国"] + :param win_size: int, like 3 + :return: yield + """ + if win_size < 2: + win_size = 2 + for i in range(1, win_size): + if i >= len(sent_words): + break + sent_terms = sent_words[i:] # 后面的 + sent_zip = zip(sent_words, sent_terms) # 候选词对 + for sz in sent_zip: + yield sz + + def keyword(self, text, num=6, score_min=0.025, win_size=3, type_sim="total", type_encode="avg", config={"alpha": 0.86, "max_iter":100}): + """ + 关键词抽取, textrank of word2vec cosine + :param text: str, doc. like "大漠帝国是历史上存在的国家吗?你知不知道？嗯。" + :param num: int, length of sentence like 6 + :param win_size: int, windows size of combine. like 2 + :param type_sim: str, type of simiilarity. like "total", "cosine" + :param config: dict, config of pagerank. like {"alpha": 0.86, "max_iter":100} + :return: list, result of keyword. like [(0.020411696169510562, '手机'), (0.016149784106276977, '夏普')] + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + # macropodus_cut 切词 + self.macropodus_word = [macropodus_cut(sentence) for sentence in self.sentences] + # 去除停用词等 + self.sentences_word = [[w for w in mw if w not in self.stop_words.values()] for mw in self.macropodus_word] + # 构建图的顶点 + word2index = {} + index2word = {} + word_index = 0 + for sent_words in self.sentences_word: + for word in sent_words: + if not word in word2index: # index + word2index[word] = word_index + index2word[word_index] = word + word_index += 1 + graph_words = np.zeros((word_index, word_index)) + # 构建图的边, 以两个词语的余弦相似度为基础 + for sent_words in self.sentences_word: + for cw_1, cw_2 in self.cut_window(sent_words, win_size=win_size): + if cw_1 in word2index and cw_2 in word2index: + idx_1, idx_2 = word2index[cw_1], word2index[cw_2] + score_w2v_cosine = self.similarity(cw_1, cw_2, type_sim=type_sim, + type_encode=type_encode) + graph_words[idx_1][idx_2] = score_w2v_cosine + graph_words[idx_2][idx_1] = score_w2v_cosine + # 构建相似度矩阵 + w2v_cosine_sim = nx.from_numpy_matrix(graph_words) + # nx.pagerank + sens_scores = nx.pagerank(w2v_cosine_sim, **config) + # 得分排序 + sen_rank = sorted(sens_scores.items(), key=lambda x: x[1], reverse=True) + # 保留topk个, 防止越界 + topk = min(len(sen_rank), num) + # 返回原句子和得分 + return [(sr[1], index2word[sr[0]]) for sr in sen_rank if len(index2word[sr[0]])>1 and score_min<=sr[1]][0:topk] + + def summarize(self, text, num=320, type_sim="cosine", type_encode="avg", config={"alpha": 0.86, "max_iter":100}): + """ + 文本摘要抽取, textrank of word2vec cosine + :param text: str, doc. like "大漠帝国是历史上存在的国家吗?你知不知道？嗯。" + :param num: int, length of sentence like 6 + :param type_sim: str, type of simiilarity. like "total", "cosine" + :param config: dict, config of pagerank. like {"alpha": 0.86, "max_iter":100} + :return: list, result of keyword. like [(0.06900223298930287, 'PageRank The PageRank Citation Ranking'), (0.08698940285163381, 'PageRank通过网络浩瀚的超链接关系来确定一个页面的等级')] + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + # 输入文本句子长度 + len_sen = len(self.sentences) + # 构建图的顶点 + sent2idx = {} + idx2sent = {} + sent_idx = 0 + for sent in self.sentences: + sent2idx[sent] = sent_idx + idx2sent[sent_idx] = sent + sent_idx += 1 + graph_sents = np.zeros((sent_idx, sent_idx)) + # 构建图的边, 以两个句子的余弦相似度为基础 + for i in range(len_sen): + for j in range(len_sen): + score_w2v_cosine = self.similarity(self.sentences[i], self.sentences[j], + type_sim=type_sim, type_encode=type_encode) + graph_sents[i][j] = score_w2v_cosine + graph_sents[j][i] = score_w2v_cosine + # 构建相似度矩阵 + w2v_cosine_sim = nx.from_numpy_matrix(graph_sents) + # nx.pagerank + sens_scores = nx.pagerank(w2v_cosine_sim, **config) + # 得分排序 + sen_rank = sorted(sens_scores.items(), key=lambda x: x[1], reverse=True) + # 保留topk个, 防止越界 + topk = min(len(sen_rank), num) + # 返回原句子和得分 + return [(sr[1], self.sentences[sr[0]]) for sr in sen_rank][0:topk] + + +if __name__ == '__main__': + text = "是上世纪90年代末提出的一种计算网页权重的算法。" \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长，" \ + "业界急需一种相对比较准确的网页重要性计算方法，" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票，" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面" \ + "和投票目标的等级来决定新的等级。简单的说，" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "PageRank The PageRank Citation Ranking: Bringing Order to the Web，" \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设，" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）；" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信息。" + trww = TextrankWord2vec() + keyword = trww.keyword(text, num=8) + summary = trww.summarize(text, num=32) + print(keyword) + print(summary) + diff --git a/macropodus/summarize/nous_base/__init__.py b/macropodus/summarize/nous_base/__init__.py new file mode 100644 index 0000000..0fd8ec0 --- /dev/null +++ b/macropodus/summarize/nous_base/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/25 21:44 +# @author :Mo +# @function : \ No newline at end of file diff --git a/macropodus/summarize/nous_base/lead_3.py b/macropodus/summarize/nous_base/lead_3.py new file mode 100644 index 0000000..21a71a3 --- /dev/null +++ b/macropodus/summarize/nous_base/lead_3.py @@ -0,0 +1,65 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/8/24 22:43 +# @author :Mo +# @function :text_summary with lead-3 + + +from macropodus.preprocess.tools_ml import cut_sentence + + +class Lead3Sum: + def __init__(self): + self.algorithm = 'lead_3' + + def summarize(self, text, type_l='mix', num=320): + """ + lead-s + :param sentences: list + :param type: str, you can choose 'begin', 'end' or 'mix' + :return: list + """ + # 切句 + if type(text) == str: + sentences = cut_sentence(text) + elif type(text) == list: + sentences = text + else: + raise RuntimeError("text type must be list or str") + # 最小句子数 + num_min = min(num, len(sentences)) + if type_l=='begin': + summers = sentences[0:num] + elif type_l=='end': + summers = sentences[-num:] + else: + summers = [sentences[0]] + [sentences[-1]] + sentences[1:num-1] + summers_s = {} + for i in range(len(summers)): # 得分计算 + if len(summers) - i == 1: + summers_s[summers[i]] = (num - 0.75) / (num + 1) + else: + summers_s[summers[i]] = (num - i - 0.5) / (num + 1) + score_sen = [(rc[1], rc[0]) for rc in sorted(summers_s.items(), key=lambda d: d[1], reverse=True)][0:num_min] + return score_sen + + +if __name__ == '__main__': + doc = "是上世纪90年代末提出的一种计算网页权重的算法。" \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长，" \ + "业界急需一种相对比较准确的网页重要性计算方法，" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票，" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面" \ + "和投票目标的等级来决定新的等级。简单的说，" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "PageRank The PageRank Citation Ranking: Bringing Order to the Web，"\ + "具体说来就是，PageRank有两个基本思想，也可以说是假设，" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）；" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信息。" + text = doc.encode('utf-8').decode('utf-8') + l3 = Lead3Sum() + for score_sen in l3.summarize(text, type_l='mix', num=320): + print(score_sen) \ No newline at end of file diff --git a/macropodus/summarize/topic_base/__init__.py b/macropodus/summarize/topic_base/__init__.py new file mode 100644 index 0000000..f6682bc --- /dev/null +++ b/macropodus/summarize/topic_base/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/29 20:35 +# @author :Mo +# @function : \ No newline at end of file diff --git a/macropodus/summarize/topic_base/topic_lda.py b/macropodus/summarize/topic_base/topic_lda.py new file mode 100644 index 0000000..65835e1 --- /dev/null +++ b/macropodus/summarize/topic_base/topic_lda.py @@ -0,0 +1,124 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/31 21:33 +# @author :Mo +# @function :topic model of LDA +# @paper :Latent Dirichlet Allocation + + +from macropodus.preprocess.tools_ml import extract_chinese, tfidf_fit +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence +# sklearn +from sklearn.feature_extraction.text import CountVectorizer +from sklearn.decomposition import LatentDirichletAllocation +import numpy as np + + +class LDASum: + def __init__(self): + self.stop_words = stop_words.values() + self.algorithm = 'lda' + + def summarize(self, text, num=8, topic_min=6, judge_topic=None): + """ + LDA + :param text: str + :param num: int + :param topic_min: int + :param judge_topic: boolean + :return: + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + len_sentences_cut = len(self.sentences) + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + self.sentences_cut = [" ".join(sc) for sc in self.sentences_cut] + # # 计算每个句子的tf + # vector_c = CountVectorizer(ngram_range=(1, 2), stop_words=self.stop_words) + # tf_ngram = vector_c.fit_transform(self.sentences_cut) + # 计算每个句子的tfidf + tf_ngram = tfidf_fit(self.sentences_cut) + # 主题数, 经验判断 + topic_num = min(topic_min, int(len(sentences_cut) / 2)) # 设定最小主题数为3 + lda = LatentDirichletAllocation(n_topics=topic_num, max_iter=32, + learning_method='online', + learning_offset=50., + random_state=2019) + res_lda_u = lda.fit_transform(tf_ngram.T) + res_lda_v = lda.components_ + + if judge_topic: + ### 方案一, 获取最大那个主题的k个句子 + ################################################################################## + topic_t_score = np.sum(res_lda_v, axis=-1) + # 对每列(一个句子topic_num个主题),得分进行排序,0为最大 + res_nmf_h_soft = res_lda_v.argsort(axis=0)[-topic_num:][::-1] + # 统计为最大每个主题的句子个数 + exist = (res_nmf_h_soft <= 0) * 1.0 + factor = np.ones(res_nmf_h_soft.shape[1]) + topic_t_count = np.dot(exist, factor) + # 标准化 + topic_t_count /= np.sum(topic_t_count, axis=-1) + topic_t_score /= np.sum(topic_t_score, axis=-1) + # 主题最大个数占比, 与主题总得分占比选择最大的主题 + topic_t_tc = topic_t_count + topic_t_score + topic_t_tc_argmax = np.argmax(topic_t_tc) + # 最后得分选择该最大主题的 + res_nmf_h_soft_argmax = res_lda_v[topic_t_tc_argmax].tolist() + res_combine = {} + for l in range(len_sentences_cut): + res_combine[self.sentences[l]] = res_nmf_h_soft_argmax[l] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + ##################################################################################### + else: + ### 方案二, 获取最大主题概率的句子, 不分主题 + res_combine = {} + for i in range(len_sentences_cut): + res_row_i = res_lda_v[:, i] + res_row_i_argmax = np.argmax(res_row_i) + res_combine[self.sentences[i]] = res_row_i[res_row_i_argmax] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + num_min = min(num, int(len_sentences_cut * 0.6)) + return score_sen[0:num_min] + + +if __name__ == '__main__': + lda = LDASum() + doc = "多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，" \ + "与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。" \ + "该基金认缴出资总规模为人民币3.01亿元。" \ + "基金的出资方式具体如下：出资进度方面，基金合伙人的出资应于基金成立之日起四年内分四期缴足，每期缴付7525万元；" \ + "各基金合伙人每期按其出资比例缴付。合伙期限为11年，投资目标为教育领域初创期或成长期企业。" \ + "截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:" \ + "公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。" \ + "方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + + doc = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + + sum = lda.summarize(doc, num=8) + for i in sum: + print(i) diff --git a/macropodus/summarize/topic_base/topic_lsi.py b/macropodus/summarize/topic_base/topic_lsi.py new file mode 100644 index 0000000..d23d0fe --- /dev/null +++ b/macropodus/summarize/topic_base/topic_lsi.py @@ -0,0 +1,98 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/2 21:03 +# @author :Mo +# @function :topic model of LSI +# @paper :Text summarization using Latent Semantic Analysis + + +from macropodus.preprocess.tools_ml import cut_sentence, macropodus_cut +from macropodus.preprocess.tools_ml import extract_chinese, tfidf_fit +from macropodus.data.words_common.stop_words import stop_words +# sklearn +from sklearn.decomposition import TruncatedSVD +import numpy as np + + +class LSISum: + def __init__(self): + self.stop_words = stop_words.values() + self.algorithm = 'lsi' + + def summarize(self, text, num=320, topic_min=5, judge_topic='all'): + """ + + :param text: + :param num: + :return: + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + len_sentences_cut = len(self.sentences) + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + self.sentences_cut = [" ".join(sc) for sc in self.sentences_cut] + # 计算每个句子的tfidf + sen_tfidf = tfidf_fit(self.sentences_cut) + # 主题数, 经验判断 + topic_num = min(topic_min, int(len(sentences_cut)/2)) # 设定最小主题数为3 + svd_tfidf = TruncatedSVD(n_components=topic_num, n_iter=32) + res_svd_u = svd_tfidf.fit_transform(sen_tfidf.T) + res_svd_v = svd_tfidf.components_ + + if judge_topic: + ### 方案一, 获取最大那个主题的k个句子 + ################################################################################## + topic_t_score = np.sum(res_svd_v, axis=-1) + # 对每列(一个句子topic_num个主题),得分进行排序,0为最大 + res_nmf_h_soft = res_svd_v.argsort(axis=0)[-topic_num:][::-1] + # 统计为最大每个主题的句子个数 + exist = (res_nmf_h_soft <= 0) * 1.0 + factor = np.ones(res_nmf_h_soft.shape[1]) + topic_t_count = np.dot(exist, factor) + # 标准化 + topic_t_count /= np.sum(topic_t_count, axis=-1) + topic_t_score /= np.sum(topic_t_score, axis=-1) + # 主题最大个数占比, 与主题总得分占比选择最大的主题 + topic_t_tc = topic_t_count + topic_t_score + topic_t_tc_argmax = np.argmax(topic_t_tc) + # 最后得分选择该最大主题的 + res_nmf_h_soft_argmax = res_svd_v[topic_t_tc_argmax].tolist() + res_combine = {} + for l in range(len_sentences_cut): + res_combine[self.sentences[l]] = res_nmf_h_soft_argmax[l] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + ##################################################################################### + else: + ### 方案二, 获取最大主题概率的句子, 不分主题 + res_combine = {} + for i in range(len_sentences_cut): + res_row_i = res_svd_v[:, i] + res_row_i_argmax = np.argmax(res_row_i) + res_combine[self.sentences[i]] = res_row_i[res_row_i_argmax] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + num_min = min(num, int(len_sentences_cut * 0.6)) + return score_sen[0:num_min] + + +if __name__ == '__main__': + lsi = LSISum() + doc = "多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，" \ + "与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。" \ + "该基金认缴出资总规模为人民币3.01亿元。" \ + "基金的出资方式具体如下：出资进度方面，基金合伙人的出资应于基金成立之日起四年内分四期缴足，每期缴付7525万元；" \ + "各基金合伙人每期按其出资比例缴付。合伙期限为11年，投资目标为教育领域初创期或成长期企业。" \ + "截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:" \ + "公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。" \ + "方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + sum = lsi.summarize(doc, num=8) + for i in sum: + print(i) diff --git a/macropodus/summarize/topic_base/topic_nmf.py b/macropodus/summarize/topic_base/topic_nmf.py new file mode 100644 index 0000000..60a0fac --- /dev/null +++ b/macropodus/summarize/topic_base/topic_nmf.py @@ -0,0 +1,137 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/2 20:33 +# @author :Mo +# @function :topic model of NMF + + +from macropodus.preprocess.tools_ml import extract_chinese, tfidf_fit +from macropodus.data.words_common.stop_words import stop_words +from macropodus.preprocess.tools_ml import macropodus_cut +from macropodus.preprocess.tools_ml import cut_sentence +# sklearn +from sklearn.decomposition import NMF +import numpy as np + + +class NMFSum: + def __init__(self): + self.stop_words = stop_words.values() + self.algorithm = 'lsi' + + def summarize(self, text, num=320, topic_min=5, judge_topic="all"): + """ + + :param text: text or list, input docs + :param num: int, number or amount of return + :param topic_min: int, topic number + :param judge_topic: str, calculate ways of topic + :return: + """ + # 切句 + if type(text) == str: + self.sentences = cut_sentence(text) + elif type(text) == list: + self.sentences = text + else: + raise RuntimeError("text type must be list or str") + # 切词 + sentences_cut = [[word for word in macropodus_cut(extract_chinese(sentence)) + if word.strip()] for sentence in self.sentences] + len_sentences_cut = len(sentences_cut) + # 去除停用词等 + self.sentences_cut = [list(filter(lambda x: x not in self.stop_words, sc)) for sc in sentences_cut] + self.sentences_cut = [" ".join(sc) for sc in self.sentences_cut] + # 计算每个句子的tfidf + sen_tfidf = tfidf_fit(self.sentences_cut) + # 主题数, 经验判断 + topic_num = min(topic_min, int(len(sentences_cut) / 2)) # 设定最小主题数为3 + nmf_tfidf = NMF(n_components=topic_num, max_iter=320) + res_nmf_w = nmf_tfidf.fit_transform(sen_tfidf.T) # 基矩阵 or 权重矩阵 + res_nmf_h = nmf_tfidf.components_ # 系数矩阵 or 降维矩阵 + + if judge_topic: + ### 方案一, 获取最大那个主题的k个句子 + ################################################################################## + topic_t_score = np.sum(res_nmf_h, axis=-1) + # 对每列(一个句子topic_num个主题),得分进行排序,0为最大 + res_nmf_h_soft = res_nmf_h.argsort(axis=0)[-topic_num:][::-1] + # 统计为最大每个主题的句子个数 + exist = (res_nmf_h_soft <= 0) * 1.0 + factor = np.ones(res_nmf_h_soft.shape[1]) + topic_t_count = np.dot(exist, factor) + # 标准化 + topic_t_count /= np.sum(topic_t_count, axis=-1) + topic_t_score /= np.sum(topic_t_score, axis=-1) + # 主题最大个数占比, 与主题总得分占比选择最大的主题 + topic_t_tc = topic_t_count + topic_t_score + topic_t_tc_argmax = np.argmax(topic_t_tc) + # 最后得分选择该最大主题的 + res_nmf_h_soft_argmax = res_nmf_h[topic_t_tc_argmax].tolist() + res_combine = {} + for l in range(len_sentences_cut): + res_combine[self.sentences[l]] = res_nmf_h_soft_argmax[l] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + ##################################################################################### + else: + ### 方案二, 获取最大主题概率的句子, 不分主题 + res_combine = {} + for i in range(len_sentences_cut): + res_row_i = res_nmf_h[:, i] + res_row_i_argmax = np.argmax(res_row_i) + res_combine[self.sentences[i]] = res_row_i[res_row_i_argmax] + score_sen = [(rc[1], rc[0]) for rc in sorted(res_combine.items(), key=lambda d: d[1], reverse=True)] + num_min = min(num, int(len_sentences_cut * 0.6)) + return score_sen[0:num_min] + + +if __name__ == '__main__': + nmf = NMFSum() + doc = "多知网5月26日消息，今日，方直科技发公告，拟用自有资金人民币1.2亿元，" \ + "与深圳嘉道谷投资管理有限公司、深圳嘉道功程股权投资基金（有限合伙）共同发起设立嘉道方直教育产业投资基金（暂定名）。" \ + "该基金认缴出资总规模为人民币3.01亿元。" \ + "基金的出资方式具体如下：出资进度方面，基金合伙人的出资应于基金成立之日起四年内分四期缴足，每期缴付7525万元；" \ + "各基金合伙人每期按其出资比例缴付。合伙期限为11年，投资目标为教育领域初创期或成长期企业。" \ + "截止公告披露日，深圳嘉道谷投资管理有限公司股权结构如下:截止公告披露日，深圳嘉道功程股权投资基金产权结构如下:" \ + "公告还披露，方直科技将探索在中小学教育、在线教育、非学历教育、学前教育、留学咨询等教育行业其他分支领域的投资。" \ + "方直科技2016年营业收入9691万元，营业利润1432万元，归属于普通股股东的净利润1847万元。（多知网 黎珊）}}" + + doc = "和投票目标的等级来决定新的等级.简单的说。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。" \ + "业界急需一种相对比较准确的网页重要性计算方法。" \ + "是人们能够从海量互联网世界中找出自己需要的信息。" \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。" \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。" \ + "一个高等级的页面可以使其他低等级页面的等级提升。" \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。" \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。" \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。" \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。" + + doc = '早年林志颖带kimi上《爸爸去哪儿》的时候，当时遮遮掩掩的林志颖老婆低调探班，总让人觉得格外神秘，大概是特别不喜欢' \ + '在公众面前曝光自己日常的那种人。可能这么些年过去，心态不断调整过了，至少在微博上，陈若仪越来越放得开，晒自己带' \ + '娃照顾双子星的点滴，也晒日常自己的护肤心得，时不时安利一些小东西。都快晚上十点半，睡美容觉的最佳时候，结果才带' \ + '完一天娃的陈若仪还是不忘先保养自己，敷起了面膜。泡完澡，这次用的是一个稍微平价的面膜，脸上、甚至仔细到脖子上都' \ + '抹上了。陈若仪也是多此一举，特别说自己不是裸体，是裹着浴巾的，谁在意这个呀，目光完全被你那又长又扑闪的睫毛给吸' \ + '引住了。这也太吓人吧，怎么能够长那么长那么密那么翘。嫉妒地说一句，真的很像种的假睫毛呐。陈若仪的睫毛应该是天生' \ + '的基础好吧，要不然也不会遗传给小孩，一家子都是睫毛精，几个儿子现在这么小都是长睫毛。只是陈若仪现在这个完美状态，' \ + '一定是后天再经过悉心的呵护培养。网友已经迫不及待让她教教怎么弄睫毛了，陈若仪也是答应地好好的。各种私人物品主动' \ + '揭秘，安利一些品牌给大家，虽然一再强调是自己的日常小物，还是很让人怀疑，陈若仪是不是在做微商当网红呐，网友建议' \ + '她开个店，看这回复，也是很有意愿了。她应该不缺这个钱才对。隔三差五介绍下自己用的小刷子之类，陈若仪乐于向大家传' \ + '授自己的保养呵护之道。她是很容易就被晒出斑的肤质，去海岛参加婚礼，都要必备这几款超爱用的防晒隔离。日常用的、太' \ + '阳大时候用的，好几个种类，活得相当精致。你们按照自己的需要了解一下。画眉毛，最爱用的是intergrate的眉笔。也是个' \ + '念旧的人，除了Dior，陈若仪的另一个眉粉其中一个是她高中就开始用的Kate。一般都是大学才开始化妆修饰自己，感受得到' \ + '陈若仪从小就很爱美。各种小零小碎的化妆品，已经买过七八次的粉红胡椒抛光美体油，每天洗完澡陈若仪都会喷在肚子、大' \ + '腿、屁股和膝盖手肘，说是能保持肌肤的平滑紧致程度。每安利一样东西，总有网友要在下面问其他问题咋个办，真是相当信' \ + '任陈若仪了。每次她也很耐心的解答，"去黑头我用的是SUQQU洁面去角质按摩膏磨砂洁面洗面奶，"一定要先按摩再用。她自己' \ + '已经回购过好几次，意思是你们再了解一下。了解归了解，买不买随意。毕竟像她另一个爱用的达尔肤面膜，效果好是好，价' \ + '格据说比sk2都还要贵，不是大多数人日常能够消费得起的，大家就看个热闹就好了，还是多买多试多用才能找到最适合自己的' \ + '护肤方法。' + + sum = nmf.summarize(doc, num=320) + for i in sum: + print(i) + + diff --git a/macropodus/tookit/__init__.py b/macropodus/tookit/__init__.py new file mode 100644 index 0000000..a5c0635 --- /dev/null +++ b/macropodus/tookit/__init__.py @@ -0,0 +1,20 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/11/28 20:49 +# @author : Mo +# @function: + + +# tookit +from macropodus.tookit.chinese2number.chinese2number import Chi2Num, Num2Chi +from macropodus.tookit.calculator_sihui.calcultor_sihui import Calculator +from macropodus.tookit.trie_tree.trie_tree import TrieTree + +# 常用工具(tookit, 计算器, 中文与阿拉伯数字转化, 前缀树) +Calcul = Calculator() +Chi2num = Chi2Num() +Num2chi = Num2Chi() +Trie = TrieTree() +calculate = Calcul.calculator_sihui +chi2num = Chi2num.compose_decimal +num2chi = Num2chi.decimal_chinese diff --git a/macropodus/tookit/calculator_sihui/__init__.py b/macropodus/tookit/calculator_sihui/__init__.py new file mode 100644 index 0000000..8396880 --- /dev/null +++ b/macropodus/tookit/calculator_sihui/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/3 20:25 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/tookit/calculator_sihui/calcultor_formula.py b/macropodus/tookit/calculator_sihui/calcultor_formula.py new file mode 100644 index 0000000..f230f06 --- /dev/null +++ b/macropodus/tookit/calculator_sihui/calcultor_formula.py @@ -0,0 +1,246 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/21 23:38 +# @author :Mo +# @function :calcultor of text, not filter and redundancy + + +from macropodus.conf.path_log import get_logger_root +import re + + +logger = get_logger_root() + + +def change_symbol(formula): + """ + 提取负号 + eg：-9-2-5-2*3-5/3-40*4/-1.0/5+6*3 ===> -(9+2+5+2*3+5/3+40*4/1.0/5-6*3) + :param formula: + :return: + """ + def primary_change(for_str): # 把算式中的全角 + - 对应换成 - + + temp = for_str.split("+") + new_formula = [] + for value in temp: + value = value.replace("-", "+") + new_formula.append(value) + return "-".join(new_formula) + + if formula.startswith("-"): + formula = formula.replace("-", "", 1) + formula = primary_change(formula) + formula = formula.join(["-(", ")"]) + elif formula.startswith("+"): + formula = primary_change(formula) + formula = formula.join(["-(", ")"]) + else: + formula = primary_change(formula) + formula = formula.join(["-(-", ")"]) + return formula + + +def remove_repeat(formula): + """ + 去掉连续的重复的运算符 + :param formula: str, like: "1++2" + :return: str, like:"1+2" + """ + temp = formula.replace("++", "+") + temp = temp.replace("+-", "-") + temp = temp.replace("-+", "-") + temp = temp.replace("--", "+") + temp = temp.replace("*+", "*") + temp = temp.replace("+*", "*") + temp = temp.replace("/+", "/") + temp = temp.replace("+/", "/") + return temp + + +def has_special_operator(formula, special_operator): + """ + 判断是否有 *+ +- /- 之类的运算符 + :param formula: + :param special_operator: + :return: + """ + for operator in special_operator: + if formula.find(operator) != -1: + return operator + return "" + + +def handle_special_operator(formula, operator): + """ + 如果有 "*-", "-*", "/-", "-/" 这些运算符， + 提取负号，去掉重复的运算符 + :param formula: + :param operator: + :return: + """ + temp = "" + regex = "\d*[.]?\d+" + opera = operator.replace("*", "[*]") + ret = re.compile(opera.join([regex, regex])) + while ret.search(formula): + search_res = ret.search(formula).group() + if operator.find("*") != -1: + temp = search_res.replace(operator, "*") + elif operator.find("/") != -1: + temp = search_res.replace(operator, "/") + temp = "-".join(["", temp]) + formula = formula.replace(search_res, temp, 1) + return formula + + +def has_parentheses(formula): + """ + 判断是否还有括号 + :param formula: str + :return: boolean + """ + if re.search("[()]", formula): + return True + return False + + +def judge_illegal(formula): + """ + 判断括号是否匹配完全，运算符是否合法 + 没有考虑 ** // 的计算 + :param formula: str + :return: str + """ + if len(re.findall("[(]", formula)) != len(re.findall("[)]", formula)): + return True + if formula.startswith("*") or formula.startswith("/"): + return True + if has_special_operator(formula, ["*/", "/*", "**", "//"]): + return True + return False + + +def calculator_formula(formula): + """ + 计算算式，这里计算的是不带括号的算式 + 计算次序是 / * - + + 计算过程中出现括号则停止计算，返回当前的算式 + :param formula: + :return: + """ + def primary_operator(for_str, operation): + try: + primary_result = 0 + regex = "\d*[.]?\d*" + ret = re.compile(operation.join(["[", "]"]).join([regex, regex])) + while ret.search(for_str): + ret_opera = has_special_operator(for_str, ["*-", "-*", "/-", "-/"]) + while ret_opera: + for_str = handle_special_operator(for_str, ret_opera) + ret_opera = has_special_operator(for_str, ["*-", "-*", "/-", "-/"]) + while has_special_operator(for_str, ["+-", "-+", "++", "--", "+*", "*+", "+/", "/+"]): + for_str = remove_repeat(for_str) + # print("primary_operator:", for_str) + if has_parentheses(for_str): + return for_str + if for_str.startswith("-"): + temp = re.findall("^-\d*[.]?\d*$", for_str) + if temp: + return temp[0] + return change_symbol(for_str) + if for_str.startswith("+"): + for_str = for_str.replace("+", "", 1) + if not ret.search(for_str): + continue + search_res = ret.search(for_str).group() + operand_list = search_res.split(operation) + if operation == "/": + primary_result = float(operand_list[0]) / float(operand_list[1]) + elif operation == "*": + primary_result = float(operand_list[0]) * float(operand_list[1]) + elif operation == "-": + primary_result = float(operand_list[0]) - float(operand_list[1]) + elif operation == "+": + primary_result = float(operand_list[0]) + float(operand_list[1]) + for_str = for_str.replace(search_res, '%f' % (primary_result), 1) + return for_str + except Exception as e: + logger.info(str(e)) + return None + try: + formula = primary_operator(formula, "/") + formula = primary_operator(formula, "*") + formula = primary_operator(formula, "-") + formula = primary_operator(formula, "+") + except Exception as e: + logger.info(str(e)) + return None + return formula + + +def remove_parentheses(formula): + """ + 去掉算式的括号，计算括号里算式 + :param formula: + :return: + """ + parentheses = re.compile("\([^()]+\)") + while parentheses.search(formula): + search_res = parentheses.search(formula).group() + for_str = re.sub("[()]", "", search_res) + if judge_illegal(for_str): + return "" + for_str = calculator_formula(for_str) + formula = formula.replace(search_res, for_str, 1) + """ + 会有去掉所有括号算式还没算完的情况 + eg：1-2*65 + 需要再计算一遍算式 + """ + formula = calculator_formula(formula) + return formula + + +def result_formula(formula): + """ + 简单计算器, 纯粹四则运算 + 去完括号后额外计算的那一次若再次出现括号， + 则重复去括号运算，直至再没有括号 + :param formula: str + :return: str + """ + + def remove_space(formula): + """ + 去掉算式的空格 + :param formula: str + :return: str + """ + return formula.replace(" ", "") + + def first_calculator(for_str): + """ + 先计算括号里边的 + :param for_str: + :return: + """ + if judge_illegal(for_str): + return None + return remove_parentheses(for_str) + + formula = remove_space(formula) + + formula = first_calculator(formula) + if not formula: + return None + while has_parentheses(formula): + formula = first_calculator(formula) + # print("calculator_result:", formula) + if not formula: + return None + return formula + + +if __name__ == '__main__': + cal = result_formula("1+1+2+3*(35+1-5*7-10/5)/2*2") + print(cal) diff --git a/macropodus/tookit/calculator_sihui/calcultor_function.py b/macropodus/tookit/calculator_sihui/calcultor_function.py new file mode 100644 index 0000000..cbd54c8 --- /dev/null +++ b/macropodus/tookit/calculator_sihui/calcultor_function.py @@ -0,0 +1,246 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/21 23:36 +# @author :Mo +# @function :function of some basic Extraction Scientific Computing + + +from macropodus.tookit.calculator_sihui.calcultor_number import extract_number +from macropodus.conf.path_log import get_logger_root +import math +import re + + +logger = get_logger_root() + + + +def rackets_replace(rackets_char, myformula): + """ + 将2(3换成2*(3, 3)4换成3)*4 + :param rackets_char: + :param myformula: + :return: + """ + if rackets_char in myformula: # "("在算式里边 + if rackets_char =="(": + rackets_re = r'\(' + else: + rackets_re = r'\)' + pos_rackets = re.finditer(rackets_re, myformula) + count = 0 + for pos in pos_rackets: + pos_single = pos.start() + count + if pos_single != 0 and rackets_char =="(": + if myformula[pos_single-1] in '零一二两三四五六七八九0123456789百十千万亿': + myformula = myformula[:pos_single] + "*" + myformula[pos_single:] + count += 1 + if pos_single != len(myformula)-1 and rackets_char ==")": + if myformula[pos_single+1] in '零一二两三四五六七八九0123456789百十千万亿': + myformula = myformula[:pos_single+1] + "*" + myformula[pos_single+1:] + count += 1 + return myformula + else: + return myformula + + + +def reagan(words, wordsminus): + """ + 求平方根，立方根，n次方根 + :param words: str, 原句 + :param wordsminus:str , 处理后的句子 + :return: + """ + try: + if '根号' in words: + reagan = wordsminus.replace("开", "").replace("根号", "").replace("的", "") + radicalaa = float(extract_number(reagan)[0]) + if radicalaa < 0.0: + return 'illegal math' + radicalbb = math.sqrt(radicalaa) + results = str(radicalbb) + elif "平方根" in words: + reagan = wordsminus.replace("开", "").replace("平方根", "").replace("平方", "").replace("的", "") + reagan = extract_number(reagan)[0] + squarerootaa = float(reagan) + if squarerootaa < 0.0: + return 'illegal math' + squarerootbb = math.sqrt(squarerootaa) + results = str(squarerootbb) + elif "立方根" in words: + reagan = wordsminus.replace("开", "").replace("立方根", "").replace("立方", "").replace("的", "") + reagan = extract_number(reagan)[0] + squarerootaa = float(reagan) + squarerootbb = math.pow(squarerootaa, 1.0 / 3) + results = str(squarerootbb) + elif "次方根" in words: + reagan = wordsminus.replace("开", "").replace("次方根", "").replace("次方", "") + squareroot = reagan.split("的") + squarerootaa = float(extract_number(squareroot[0])[0]) + squarerootbb = float(extract_number(squareroot[1])[0]) + if squarerootaa % 2 == 0 and squarerootbb < 0.0: + return 'illegal math' + squarerootcc = math.pow(squarerootaa, 1.0 / squarerootbb) + results = str(squarerootcc) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +def power(words, wordsminus): + """ + 求指数，求平方 + :param words: + :param wordsminus: + :return: + """ + try: + if "平方根" not in words and "平方" in words: + reagan = wordsminus.replace("平方", "").replace("开", "").replace("的", "") + reagan = extract_number(reagan)[0] + square = float(reagan) + radicalbb = math.pow(square, 2) + results = str(radicalbb) + elif "立方根" not in words and "立方" in words: + reagan = wordsminus.replace("立方", "").replace("开", "").replace("的", "") + reagan = extract_number(reagan)[0] + square = float(reagan) + radicalbb = math.pow(square, 3) + results = str(radicalbb) + elif (("次方" in words or "次幂" in words) and "次方根" not in words and "次幂根" not in words): + reagan = wordsminus.replace("次方", "").replace("开", "").replace("次幂", "") + squareroot = reagan.split("的") + squarerootaa = float(extract_number(squareroot[0])[0]) + squarerootbb = float(extract_number(squareroot[1])[0]) + squarerootcc = math.pow(squarerootaa, squarerootbb) + results = str(squarerootcc) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +def logarithm(words, wordsminus): + """ + 求对数 + :param words: + :param wordsminus: + :return: + """ + try: + if "LG" in words or "LOG" in words: + Lg = wordsminus.replace("LOG", "").replace("LG", "").replace(" ", "").replace("的", "") + Lg = float(extract_number(Lg)[0]) + if Lg <= 0.0: + return 'illegal math' + lgbb = math.log(Lg) + results = str(lgbb) + elif "对数" in words: + Logg = wordsminus.replace("以", "").replace("对数", "").replace("的对数", "").replace(" ", "").replace("的", "") + root = Logg.split("为底") + rootaa = float(extract_number(root[0])[0]) + rootbb = float(extract_number(root[1])[0]) + if rootaa <= 0.0 or rootbb <= 0.0: + return 'illegal math' + rootcc = math.log(rootbb) / math.log(rootaa) + results = str(rootcc) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +def fraction(words, wordsminus): + """ + 求分数 + :param words: + :param wordsminus: + :return: + """ + try: + if "fenzhi" in words: + fenzhi = wordsminus.replace("fenzhi", "/").replace(" ", "").replace("的", "") + root = fenzhi.split("/") + rootaa = float(extract_number(root[0])[0]) + rootbb = float(extract_number(root[1])[0]) + rootcc = rootbb / rootaa + results = str(rootcc) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +def fractiontwo(words, wordsminus): + """ + 取分数 + :param words: + :param wordsminus: + :return: + """ + try: + if "fenzhi" in words: + fenzhi = wordsminus.replace("fenzhi", "/").replace(" ", "").replace("的", "") + root = fenzhi.split("/") + rootaa = float(extract_number(root[0])[0]) + rootbb = float(extract_number(root[1])[0]) + results = str(rootaa/rootbb) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +def factorial(words, wordsminus): + """ + 求阶乘 + :param words: + :param wordsminus: + :return: + """ + results = words + try: + if "jiecheng的" in words: + factory = wordsminus.replace("jiecheng的", "").replace("的", "").replace(" ", "") + fact = float(extract_number(factory)[0]) + if fact <= 10000: + results = str(math.factorial(fact)) + else: + results = words + return results + except Exception as e: + logger.info(str(e)) + return words + + +if __name__ == '__main__': + res = reagan("根号4", "根号4") + print(res) + res = reagan("27的3次方根是多少", "27的3次方根") + print(res) + res = power("9的平方", "9的平方") + print(res) + res = power("27的立方是几", "9的立方") + print(res) + res = power("3的3次方是几", "3的3次方实") + print(res) + res = logarithm("LG8", "LG8") + print(res) + res = logarithm("以2为底64的对数", "以2为底64的对数") + print(res) + res = fraction("1fenzhi6是多少", "1fenzhi6") + print(res) + res = factorial("10jiecheng的", "10jiecheng的") + print(res) diff --git a/macropodus/tookit/calculator_sihui/calcultor_number.py b/macropodus/tookit/calculator_sihui/calcultor_number.py new file mode 100644 index 0000000..0a46b85 --- /dev/null +++ b/macropodus/tookit/calculator_sihui/calcultor_number.py @@ -0,0 +1,210 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/22 20:09 +# @author :Mo +# @function :extract number from sentence of chinese or mix。提取数字,中文，或者混合中文-阿拉伯数字 + + +import regex as re +# import re + + +# * 字符串预处理模块，为分析器TimeNormalizer提供相应的字符串预处理服务 +class StringPreHandler: + # @Author : zhm + # @codes : code from github: https://github.com/zhanzecheng/Time_NLP + # @function :StringPreHandler.py + @classmethod + def delKeyword(cls, target, rules): + """ + 该方法删除一字符串中所有匹配某一规则字串 + 可用于清理一个字符串中的空白符和语气助词 + :param target: 待处理字符串 + :param rules: 删除规则 + :return: 清理工作完成后的字符串 + """ + pattern = re.compile(rules) + res = pattern.sub('', target) + # print res + return res + + + @classmethod + def numberTranslator(cls, target): + """ + 该方法可以将字符串中所有的用汉字表示的数字转化为用阿拉伯数字表示的数字 + 如"这里有一千两百个人，六百零五个来自中国"可以转化为 + "这里有1200个人，605个来自中国" + 此外添加支持了部分不规则表达方法 + 如两万零六百五可转化为20650 + 两百一十四和两百十四都可以转化为214 + 一六零加一五八可以转化为160+158 + 该方法目前支持的正确转化范围是0-99999999 + 该功能模块具有良好的复用性 + :param target: 待转化的字符串 + :return: 转化完毕后的字符串 + """ + pattern = re.compile(u"[一二两三四五六七八九123456789]万[一二两三四五六七八九123456789](?!(千|百|十))") + match = pattern.finditer(target) + for m in match: + group = m.group() + s = group.split(u"万") + s = list(filter(None, s)) + num = 0 + if len(s) == 2: + num += cls.wordToNumber(s[0]) * 10000 + cls.wordToNumber(s[1]) * 1000 + target = pattern.sub(str(num), target, 1) + + pattern = re.compile(u"[一二两三四五六七八九123456789]千[一二两三四五六七八九123456789](?!(百|十))") + match = pattern.finditer(target) + for m in match: + group = m.group() + s = group.split(u"千") + s = list(filter(None, s)) + num = 0 + if len(s) == 2: + num += cls.wordToNumber(s[0]) * 1000 + cls.wordToNumber(s[1]) * 100 + target = pattern.sub(str(num), target, 1) + + pattern = re.compile(u"[一二两三四五六七八九123456789]百[一二两三四五六七八九123456789](?!十)") + match = pattern.finditer(target) + for m in match: + group = m.group() + s = group.split(u"百") + s = list(filter(None, s)) + num = 0 + if len(s) == 2: + num += cls.wordToNumber(s[0]) * 100 + cls.wordToNumber(s[1]) * 10 + target = pattern.sub(str(num), target, 1) + + pattern = re.compile(u"[零一二两三四五六七八九]") + match = pattern.finditer(target) + for m in match: + target = pattern.sub(str(cls.wordToNumber(m.group())), target, 1) + + pattern = re.compile(u"(?<=(周|星期))[末天日]") + match = pattern.finditer(target) + for m in match: + target = pattern.sub(str(cls.wordToNumber(m.group())), target, 1) + + pattern = re.compile(u"(? + """ + res = sph.numberTranslator(target=sentence) + find_list = [] + for i in re.finditer('(\d+(\.\d+)?)', res): + find_list.append(i.group()) + return find_list + +if __name__ == '__main__': + sen = "1000.一加1等于几" + res = extract_number(sen) + print(res) diff --git a/macropodus/tookit/calculator_sihui/calcultor_sihui.py b/macropodus/tookit/calculator_sihui/calcultor_sihui.py new file mode 100644 index 0000000..dff3ed8 --- /dev/null +++ b/macropodus/tookit/calculator_sihui/calcultor_sihui.py @@ -0,0 +1,224 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/21 20:22 +# @author :Mo +# @function :an ai text calcultor of xiaomo + + +from macropodus.tookit.calculator_sihui.calcultor_function import rackets_replace, reagan, power, logarithm, fraction, factorial, fractiontwo +from macropodus.tookit.calculator_sihui.calcultor_number import extract_number, sph +from macropodus.tookit.calculator_sihui.calcultor_formula import result_formula +from macropodus.conf.path_log import get_logger_root +import re + + +logger = get_logger_root() + + +def StringToCalculateZero(words=''): + """ + 混合运算去除非计算式等无用词 + :param words: str + :return: str + """ + wordsspot = words.replace("点", ".") + wordsmark = wordsspot.replace("分之", "fenzhi") + wordsin = wordsmark.replace("正切", "zheng切").replace("正弦", "zheng弦").replace("正割", "zheng割").replace("正矢", "zheng矢") + wordsadd = wordsin.replace("加上", "+").replace("加到", "+").replace("加", "+").replace("＋", "+").replace("正", "+") + wordsminus = wordsadd.replace("减去", "-").replace("减", "-").replace("－", "-").replace("负", "-") + wordsmult = wordsminus.replace("阶乘", "jiecheng的").replace("乘上", "*").replace("乘以", "*").replace("乘于","*").replace("乘", "*").replace("×", "*") + wordsdivis01 = wordsmult.replace("除去", "/").replace("除以", "/").replace("除于", "/").replace("除","/").replace("÷", "/") + wordsdivis02 = wordsdivis01.replace("从", "").replace("再", "").replace("在", "").replace("然后", "").replace("直", "").replace("到", "") + wordbrackets = wordsdivis02.replace("（", "(").replace("）", ")").replace("=", "").replace("=", "") + formula = wordbrackets.replace("左括号", "(").replace("右括号", "(").replace("的和", "").replace("的差", "").replace("的商", "").replace("的积", "") + myformula_1 = formula.replace("*-", "*(-1)*").replace("\\*\\+", "*").replace("\\/\\-", "/(-1)/") + myformula_2 = myformula_1.replace(" ", "").replace("\\+\\-", "\\-").replace("\\+\\+", "\\+").replace("\\-\\+", "\\-").replace("\\-\\-", "\\+") + myformula_2 = rackets_replace("(", myformula_2) + myformula_2 = rackets_replace(")", myformula_2) + + return myformula_2 + + +def StringToCalculateOne(words): + """ + 简单句总调用 + 求乘方，阶乘，指数，根式，三角函数，对数，最大最小公约数公倍数 + :param words: str + :return: str + """ + try: + res_reagan = reagan(words, words) # 报错或不执行返回原来的数据 + res_power = power(words, words) + # aa22 = triangle(complex[i], complex[i]) + res_logarithm = logarithm(words, words) + rees_factorial = factorial(words, words) + res_fraction = fraction(words, words) + if (res_reagan != words): + goal = res_reagan + elif (res_power != words): + goal = res_power + # elif (aa22 != complex[i]): + # goal = aa22 + elif (res_logarithm != words): + goal = res_logarithm + elif (rees_factorial != words): + goal = rees_factorial + elif (res_fraction != words): + goal = res_fraction + else: + oldwords = words.replace("的", "") + oldwords = extract_number(oldwords)[0] + goal = oldwords + return goal + except Exception as e: + logger.info(str(e)) + return words + + +def StringToCalculateTwo(sentence=''): + """ + 复杂算式, 总调用， 分步计算，先计算三角函数，指数，对数 + 1.取出操作符与数据（注意--，++，-，+开头这种） + 2.计算中间的，比如说根号12，2的7次方这种 + :param sentence: + :return: + """ + try: + if sentence[0] == '+' or sentence[0] == '-': + sentence = '0' + sentence + minus = 0 + operators = [] + complex = re.split("[+*/-]", sentence) + for s in sentence: + if ((s == '+' or s == '-' or s == '*' or s == '/') & minus != 0 & minus != 2): + operators.append("" + s) + minus = minus + 1 + else: + minus = 1 + # complex.append(float(formula[prePos:].strip())) + formula = "" + for i in range(len(complex)): + if "" == complex[i]: + complex[i] = " " + formula = formula + complex[i] + operators[i] + continue + res_reagan = reagan(complex[i], complex[i]) #报错或不执行返回原来的数据 + res_power = power(complex[i], complex[i]) + # aa22 = triangle(complex[i], complex[i]) + res_logarithm = logarithm(complex[i], complex[i]) + res_factorial = factorial(complex[i], complex[i]) + res_fraction = fraction(complex[i], complex[i]) + + if (res_reagan != complex[i]): + goal = res_reagan + elif (res_power != complex[i]): + goal = res_power + # elif (aa22 != complex[i]): + # goal = aa22 + elif (res_logarithm != complex[i]): + goal = res_logarithm + elif (res_factorial != complex[i]): + goal = res_factorial + elif (res_fraction != complex[i]): + goal = res_fraction + elif "(" in complex[i] or ")" in complex[i]: + goal = sph.numberTranslator(target=complex[i].replace("的", "")) + else: + oldwords = complex[i].replace("的", "") + oldwords = extract_number(oldwords)[0] + goal = oldwords + if goal == 'illegal math': #非法抛出 + return 'illegal math' + if (i < len(complex) - 1): + rest = goal + operators[i] + else: + rest = goal + formula = formula + rest + myformula = formula.replace("*-", "*(-1)*").replace("*+", "*").replace("/-", "/(-1)/") + formulalast = myformula.replace(" ", "").replace("+-", "-").replace("++", "+").replace("-+", "-").replace("--","+") + except Exception as e: + logger.info(str(e)) + return sentence + + return formulalast + + +class Calculator: + def __init__(self): + self.tookit = "calculator_sihui" + + def calculator_sihui(self, sentence = ''): + """ + 思慧计算器总调用接口 + :param sentence:str, 输入句子,TEXT + :return: + """ + # 运算符转换 + sentence_wise = StringToCalculateZero(sentence) + if not sentence_wise: + return sentence + # 混合运算 + sentence_replace = StringToCalculateTwo(sentence_wise) + if ('/0' in sentence_replace and '/0.' not in sentence_replace) or sentence_replace == 'illegal math': + return 'illegal math' + for char in sentence_replace: + if char not in '+-*/().0123456789': + return 'could not calculate' + # + result = result_formula(sentence_replace) + return result + + +if __name__ == "__main__": + cal = Calculator() + equation_sample = [ + '', + '2(3*4)6+4(4)4', + '（1+2）=', + '1+2等于几', + '100+30', + '111+90-9等于几', + "23 + 13 * ((25+(-9-2-5-2*3-6/3-40*4/(2-3)/5+6*3) * (9-2*6/3 + 5 *3*9/9*5 +10 * 56/(-14) )) - (-4*3)/ (3+3*3) )", + '1-2-3-4-5-6', + '134+123*898*123456789212310', + '4*5*6/6/5', + '(1+(2+(3-(4*6/4/3)-1)-1)-3)+(6-7)', + '1+1+1', + '1*1*2', + '1+2+3+4+6', + '1/2+2*3*4*5/5/4-1', + '1+2(12/13)', + '1+2+3+4+5+6+7+8+9+10', + '-1+1+2+(-2)', + '((-3)*2+1/2-3*4/4) +1', + 'LG0', + 'LOG100', + '以2为底4的对数', + '根号一百二十三加上1', + '1加2的根号', + '根号1加2的和', + '以2为底4的对数', + '2的六次方', + '2的3次幂', + '二的一次方', + '四的平方', + '十一的立方', + '开11的立方根', + '开3的7次方根', + '13的阶乘', + '根号四', + '1除以0', + + '负一加上100加上50000', + '2的8次方减6', + '根号5乘以90', + '2的8次方减6', + '1的平方加根号2', + '30的阶乘加90', + '二分之一加1/3', + '' + ] + + for es in equation_sample: + print('ff算式: ' + es) + print('思慧计算器结果: ' + str(cal.calculator_sihui(es))) \ No newline at end of file diff --git a/macropodus/tookit/chinese2number/__init__.py b/macropodus/tookit/chinese2number/__init__.py new file mode 100644 index 0000000..4881dd1 --- /dev/null +++ b/macropodus/tookit/chinese2number/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:00 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/tookit/chinese2number/chinese2number.py b/macropodus/tookit/chinese2number/chinese2number.py new file mode 100644 index 0000000..3ff2494 --- /dev/null +++ b/macropodus/tookit/chinese2number/chinese2number.py @@ -0,0 +1,283 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/17 22:17 +# @author :Mo +# @function :change chinese digit to Arab or reversed + + +from macropodus.preprocess.tools_clear import is_total_num +import random + + +# chinese_to_number, 单位-数字 +unit_dict = {"十": 10, "百": 100, "千": 1000, "万": 10000, "亿": 100000000} +unit_dict_keys = unit_dict.keys() +digit_dict = {"零": 0, "一": 1, "二": 2, "两": 2, "俩": 2, "三": 3, + "四": 4, "五": 5, "六": 6, "七": 7, "八": 8, "九": 9} + +# number_to_chinese, 单位-数字 +num_dict = { 0: "零", 1: "一", 2: "二", 3: "三", 4: "四", + 5: "五", 6: "六", 7: "七", 8: "八", 9: "九" } +unit_map = [ ["", "十", "百", "千"], ["万", "十万", "百万", "千万"], + ["亿", "十亿", "百亿", "千亿"], ["兆", "十兆", "百兆", "千兆"] ] +unit_step = ["万", "亿", "兆"] + + +class Chi2Num(): + def __init__(self): + self.result = 0.0 + self.result_last = 0.0 + # 字符串分离 + self.str_billion = "" # 亿 + self.str_billion_hundred = "" # 亿万 + self.str_billion_one = "" + self.str_thousand_ten = "" # 万 + self.str_single = "" # one + + def free_zero_and_split_three_parts(self, text): + """ + 去零切分成三部分 + :param text:str + :return: + """ + assert type(text) == str + if "零" in text: + text = text.replace("零", "") + # 分切成三部分 + index = 0 + flag_billion = True # 亿 + flag_billion_hundred = True # 万亿 + flag_thousand_ten = True #万 + len_text = len(text) + for i in range(len_text): + if "亿" == text[i]: + # 存在亿前面也有万的情况，小分节 + self.str_billion = text[0:i] + if text.find("亿") > text.find("万"): + for j in range(len(self.str_billion)): + if "万" == self.str_billion[j]: + flag_billion_hundred = False + self.str_billion_hundred = self.str_billion[0:j] + self.str_billion_one = self.str_billion[j+1:] + # 如何亿字节中没有万，直接赋值 + if flag_billion_hundred: + self.str_billion_one = self.str_billion + index = i + 1 + flag_billion = False + # 分节完毕 + self.str_single = text[i + 1:] + if "万" == text[i] and text.find("亿") < text.find("万"): + self.str_thousand_ten = text[index:i] + self.str_single = text[i+1:] + flag_thousand_ten = False + if flag_billion and flag_thousand_ten: + self.str_single = text + + def str_to_number(self, text): + """ + string change to number + :param text: str + :return: + """ + assert type(text) == str + number_res = 0 + number_1 = 0 + number_2 = 0 + number_3 = 0 + if not text: + return 0 + len_text = len(text) + for i in range(len_text): + # 数字 + if text[i] in digit_dict: + number_1 = digit_dict[text[i]] + if i == len_text - 1: + number_res += number_1 + # 单位 + elif text[i] in unit_dict: + number_2 = unit_dict[text[i]] + if number_1==0 and number_2==10: + number_3 = number_2 + else: + number_3 = number_1 * number_2 + # 清零避免重复读取 + number_1 = 0 + number_res += number_3 + # 处理形如 "二点13亿", "1.56万" 这样的情况 + else: + try: + text_else_str = [str(digit_dict[tet]) if tet in digit_dict else tet for tet in text] + number_res = float("".join(text_else_str)) + except: + number_res = 0 + return number_res + + def compose_integer(self, text): + """ + 整数转数字, 合并 + :param text:str, input of chinese, eg.["一百", "三千零七十八亿三千零十五万零三百一十二"] + :return: float, result of change chinese to digit + """ + assert type(text) == str + self.result = 0.0 + self.result_last = 0.0 + + text = text.replace("兆", "万亿").replace("点", ".").strip(".").strip() + len_text = len(text) + # 判断十百千万在不在text里边，在的话就走第二个 + flag_pos = True + for unit_dict_key in unit_dict_keys: + if unit_dict_key in text: + flag_pos = False + break + # 分三种情况，全数字返回原值，有中文unit_dict_keys就组合， 没有中文unit_dict_keys整合 + if is_total_num(text): + digit_float = float(text) + return digit_float + elif flag_pos: + result_pos = "" + for i in range(len_text): + if "."!=text[i] and not text[i].isdigit(): + result_pos += str(digit_dict[text[i]]) + else: + result_pos += text[i] + self.result_last = float(result_pos) + else: + self.free_zero_and_split_three_parts(text) + float_billion_hundred = self.str_to_number(self.str_billion_hundred) + float_billion_one = self.str_to_number(self.str_billion_one) + float_thousand_ten = self.str_to_number(self.str_thousand_ten) + float_single = self.str_to_number(self.str_single) + + self.result = float((float_billion_hundred * 10000 + float_billion_one) * 100000000 + float_thousand_ten * 10000 + float_single) + self.result_last = self.result + # 重置 + self.str_billion = "" # 亿 + self.str_billion_hundred = "" # 亿万 + self.str_billion_one = "" + self.str_thousand_ten = "" # 万 + self.str_single = "" # one + return self.result_last + + def compose_decimal(self, text): + """ + 中文小数转数字 + :param text:str, input of chinese, eg.["一百", "三千零七十八亿三千零十五万零三百一十二"] + :return: float, result of change chinese to digit + """ + assert type(text) == str + self.result = 0.0 + self.result_last = 0.0 + self.result_start = 0.0 + + text = text.replace("兆", "万亿").replace("点", ".").strip() + if "." in text: + # 判断十百千万在不在.号后边，在的话就走compose_integer()，并且返回 + pos_point = text.find(".") + for unit_dict_key in unit_dict_keys: + if unit_dict_key in text: + if pos_point < text.find(unit_dict_key): + return self.compose_integer(text) + # 否则就是有小数 + texts = text.split(".") + text_start = texts[0] + text_end = texts[1] + + # 处理整数部分 + if "0"==text_start or "零"==text_start: + self.result_start = "0." + else: + self.result_start = str(int(self.compose_integer(text_start))) + "." + # 处理尾部，就是后边小数部分 + result_pos = "" + len_text = len(text_end) + for i in range(len_text): + if "."!=text_end[i] and not text_end[i].isdigit(): + result_pos += str(digit_dict[text_end[i]]) + else: + result_pos += text_end[i] + # 拼接 + self.result_last = float(self.result_start + result_pos) if result_pos.isdigit() else self.result_start + + return self.result_last + + +class Num2Chi(): + """ + codes reference: https://github.com/tyong920/a2c + """ + def __init__(self): + self.result = "" + + def number_to_str(self, data): + assert type(data) == float or int + res = [] + count = 0 + # 倒转 + str_rev = reversed(str(data)) # seq -- 要转换的序列，可以是 tuple, string, list 或 range。返回一个反转的迭代器。 + for i in str_rev: + if i is not "0": + count_cos = count // 4 # 行 + count_col = count % 4 # 列 + res.append(unit_map[count_cos][count_col]) + res.append(num_dict[int(i)]) + count += 1 + else: + count += 1 + if not res: + res.append("零") + elif res[-1] is not "零": + res.append("零") + # 再次倒序，这次变为正序了 + res.reverse() + # 去掉"一十零"这样整数的“零” + if res[-1] is "零" and len(res) is not 1: + res.pop() + + return "".join(res) + + def decimal_chinese(self, data): + assert type(data) == float or int + data_str = str(data) + if "." not in data_str: + res = self.number_to_str(data_str) + else: + data_str_split = data_str.split(".") + if len(data_str_split) is 2: + res_start = self.number_to_str(data_str_split[0]) + res_end = "".join([num_dict[int(number)] for number in data_str_split[1]]) + res = res_start + random.sample(["点", "."], 1)[0] + res_end + else: + res = str(data) + return res + + +if __name__=="__main__": + ###### 1.测试阿拉伯数字转中文 ###################################################### + ntc = Num2Chi() + for i in range(1945, 2100): + print(ntc.decimal_chinese(i)) + print(ntc.decimal_chinese(0.112354)) + print(ntc.decimal_chinese(1024.112354)) + + + + ###### 2.测试中文转阿拉伯 ######################################################## + ctn = Chi2Num() + tet_base = [ "1.3", "一.12", "一", "一十五", "十五", "二十", "二十三", "一百","一百零一", + "一百一十", "一百一十一", "一千", "一千零一","一千零三十一", + "一万零二十一", "一万零三百二十一", "一万一千三百二十一", "三千零十五万", + "三千零一十五万", "三千五百六十八万零一百零一", "五十亿三千零七十五万零六百二十二", + "十三亿三千零十五万零三百一十二", "一千二百五十八亿","三千零十五万零三百一十二", + "一千二百五十八万亿", "三千三百二十一", "三百三十一", "二十一", "三百二十一", + "一千二百五十八亿零三千三百二十一", "两百", "两千两百二十二", "两亿两千万两百万两百二十二", + "三千零七十八亿三千零十五万零三百一十二"] + + tet_decimal = ["1.3", "三千零七十八亿三千零十五万零三百一十二", "二点13亿", "1.56万", "十八.12", "一十八点九一", "零点一", "零点123", "十八点一", "零", "一千零九十九点六六"] + # 测试是小数 + for tet_d in tet_decimal: + print(ctn.compose_decimal(tet_d)) + # 测试是整数 + for tet_b in tet_base: + print(ctn.compose_integer(tet_b)) + diff --git a/macropodus/tookit/trie_tree/__init__.py b/macropodus/tookit/trie_tree/__init__.py new file mode 100644 index 0000000..53a832f --- /dev/null +++ b/macropodus/tookit/trie_tree/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/5 22:06 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus/tookit/trie_tree/trie_tree.py b/macropodus/tookit/trie_tree/trie_tree.py new file mode 100644 index 0000000..d59e189 --- /dev/null +++ b/macropodus/tookit/trie_tree/trie_tree.py @@ -0,0 +1,150 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/11/19 20:40 +# @author :Mo +# @function :TrieTree of keywords find, 只返回查全的情况, 查找句子中的关键词（例如影视名、人名、关键词、实体等） + + +class TrieNode: + """ + 前缀树节点-链表 + """ + def __init__(self): + self.child = {} + + +class TrieTree: + """ + 前缀树构建, 新增关键词, 关键词词语查找等 + """ + def __init__(self): + self.root = TrieNode() + + def add_keyword(self, keyword): + """ + 新增一个关键词 + :param keyword: str, 构建的关键词 + :return: None + """ + node_curr = self.root + for word in keyword: + if node_curr.child.get(word) is None: + node_next = TrieNode() + node_curr.child[word] = node_next + node_curr = node_curr.child[word] + # 每个关键词词后边, 加入end标志位 + if node_curr.child.get('[END]') is None: + node_next = TrieNode() + node_curr.child['[END]'] = node_next + node_curr = node_curr.child['[END]'] + + # def delete_keyword(self, keyword): + # """ + # 删除一个关键词 + # :param keyword: str, 构建的关键词 + # :return: None + # """ + # node_curr = self.root + # for word in keyword: + # if node_curr.child.get(word) is not None: + # node_curr = node_curr.child[word] + # # 每个关键词词后边, 加入end标志位 + # if node_curr.child.get('[END]') is not None: + + + def add_keywords_from_list(self, keywords): + """ + 新增关键词s, 格式为list + :param keyword: list, 构建的关键词 + :return: None + """ + for keyword in keywords: + self.add_keyword(keyword) + + def find_keyword(self, sentence): + """ + 从句子中提取关键词, 可提取多个 + :param sentence: str, 输入的句子 + :return: list, 提取到的关键词 + """ + assert type(sentence) == str + if not sentence: # 空格字符不取 + return [] + + node_curr = self.root # 关键词的头, 每遍历完一遍后需要重新初始化 + index_last = len(sentence) + keyword_list = [] + keyword = '' + count = 0 + for word in sentence: + count += 1 + if node_curr.child.get(word) is None: # 查看有无后缀, 即匹配到一个关键词最后一个字符的时候 + if keyword: # 提取到的关键词(也可能是前面的几位) + if node_curr.child.get('[END]') is not None: # 取以end结尾的关键词 + keyword_list.append(keyword) + if self.root.child.get(word) is not None: # 处理连续的关键词情况, 如"第九区流浪地球" + keyword = word + node_curr = self.root.child[word] + else: # + keyword = '' + node_curr = self.root # 重新初始化 + else: # 有后缀就加到name里边 + keyword = keyword + word + node_curr = node_curr.child[word] + if count == index_last: # 实体结尾的情况 + if node_curr.child.get('[END]') is not None: + keyword_list.append(keyword) + return keyword_list + + def match_keyword(self, keyword): + """ + 判断keyword在不在trietree里边 + :param keyword: str, input word + :return: boolean, True or False + """ + node = self.root + for kw in keyword: + if not node.child.get(kw): + return False + node = node.child[kw] + if not node.child.get('[END]'): + return False + return True + + +def get_trie_tree_class(keywords): + """ + 根据list关键词，初始化trie树 + :param keywords: list, input + :return: objext, 返回实例化的trie + """ + trie = TrieTree() + trie.add_keywords_from_list(keywords) + return trie + + +if __name__ == "__main__": + # 测试1, class实例 + trie = TrieTree() + keywords = ['英雄', '人在囧途', '那些年,我们一起追过的女孩', '流浪地球', '华娱', + '犬夜叉', '火影', '名侦探柯南', '约会大作战', '名作之壁', '动漫', + '乃木坂46', 'akb48', '飘', '最后的武士', '约会', '英雄2', '日娱', + '2012', '第九区', '星球大战', '侏罗纪公园', '泰坦尼克号', 'Speed'] + keywords = [list(keyword.strip()) for keyword in keywords] + trie.add_keywords_from_list(keywords) # 创建树 + keyword = trie.find_keyword('第九区约会, 侏罗纪公园和泰坦尼克号泰坦尼克号') + print(keyword) + + keyword = trie.match_keyword('第九') + print(keyword) + + + # 测试2, get树 + trie_tree = get_trie_tree_class(keywords) # 创建树并返回实例化class + while True: + print("sihui请你输入:") + input_ques = input() + keywords = trie_tree.find_keyword(input_ques) + print(keywords) + + diff --git a/macropodus/version.py b/macropodus/version.py new file mode 100644 index 0000000..a44767f --- /dev/null +++ b/macropodus/version.py @@ -0,0 +1,8 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/21 22:24 +# @author : Mo +# @function: version of Macropodus + + +__version__ = "0.0.2" diff --git a/macropodus_images/__init__.py b/macropodus_images/__init__.py new file mode 100644 index 0000000..8f6f4c4 --- /dev/null +++ b/macropodus_images/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/20 9:54 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus_images/macropodus_logo.png b/macropodus_images/macropodus_logo.png new file mode 100644 index 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b/macropodus_preprocess/__init__.py new file mode 100644 index 0000000..45e0dd1 --- /dev/null +++ b/macropodus_preprocess/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/21 23:11 +# @author : Mo +# @function: \ No newline at end of file diff --git a/macropodus_preprocess/create_segnment_data.py b/macropodus_preprocess/create_segnment_data.py new file mode 100644 index 0000000..2adbb15 --- /dev/null +++ b/macropodus_preprocess/create_segnment_data.py @@ -0,0 +1,33 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/21 23:11 +# @author : Mo +# @function: + + +from macropodus.preprocess.tools_common import load_json, save_json +from macropodus.preprocess.tools_common import txt_write, txt_read +import json + +pku_training = txt_read("pku_training.utf8") +file = open("pku_train.json", "w", encoding="utf-8") +pku_ = [] +for pku in pku_training: + pkus = pku.split(" ") + label_pkus = "" + for pku_sig in pkus: + len_pku = len(pku_sig) + if len_pku==1: + label_pkus += "S" + elif len_pku==2: + label_pkus += "BE" + else: + label_pkus += "B" + "M"*(len_pku-2) + "E" + label_pkus_l = list(label_pkus) + pku_res = {} + pku_res["question"] = list("".join(pkus)) + pku_res["label"] = label_pkus_l + p_json = json.dumps(pku_res, ensure_ascii=False) + file.write(p_json + "\n") +# pku_.append(pku_res) +# save_json(pku_, "pku_train.json") diff --git a/macropodus_preprocess/pku_training.utf8 b/macropodus_preprocess/pku_training.utf8 new file mode 100644 index 0000000..8b84132 --- /dev/null +++ b/macropodus_preprocess/pku_training.utf8 @@ -0,0 +1,5 @@ +迈向 充满 希望 的 新 世纪 —— 一九九八年 新年 讲话 （ 附 图片 １ 张 ） +中共中央 总书记 、 国家 主席 江 泽民 +（ 一九九七年 十二月 三十一日 ） +１２月 ３１日 ， 中共中央 总书记 、 国家 主席 江 泽民 发表 １９９８年 新年 讲话 《 迈向 充满 希望 的 新 世纪 》 。 +同胞 们 、 朋友 们 、 女士 们 、 先生 们 ： diff --git a/macropodus_survey_report/nlp_platfom_survey.md b/macropodus_survey_report/nlp_platfom_survey.md new file mode 100644 index 0000000..8051bfc --- /dev/null +++ b/macropodus_survey_report/nlp_platfom_survey.md @@ -0,0 +1,91 @@ +# 中文自然语言处理(nlp)工具调研与汇总(截至2019.11.16) + + +## 1.常见平台与功能 +平台|语言|star|year|中文分词|词性标注|依存句法|实体识别|关键词提取|文本摘要|文本聚类|情感识别|文本相似|关系抽取|free| +---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- +jieba|python|20.8k|7/0.5|是|是|否|否|是|否|否|是|否|否|MIT +THULAC-Python|python|1.2k|4/1|是|是|否|否|否|否|否|否|否|否|MIT +pkuseg-python|python|4.3k|0.9/0.5|是|是|否|否|否|否|否|否|否|否|MIT +snownlp|python|4.4k|6/3/*|是|是|否|否|是|是|否|是|是|否|MIT +deepnlp|python|1.3k|2/2/!|是|是|是|是|是|是|否|否|否|否|MIT +fastNLP|python|0.9k|2/0|是|是|否|是|否|否|否|是|否|否|MIT +Jiagu|python|0.97k|0.9/0|是|是|是|是|是|是|是|是|否|是|MIT +YaYaNLP|python|0.05k|4/4/!|是|是|否|是|否|否|否|否|否|否|MIT +HanLP|java|16.4k|0.9/0|是|是|是|是|是|是|是|是|否|否|MIT +ansj-seg|java|5.2k|3/0.4|是|是|是|是|是|是|否|是|否|否|Apache-2.0 +word|java|1.4k|5/1|是|是|否|是|否|否|否|否|是|否|Apache-2.0 +Jcseg|java|0.69k|3/0|是|是|是|是|是|是|否|否|否|否|Apache-2.0 +ik-analyzer|java|0.53k|9/9/!|是|是|是|否|否|否|否|否|否|否|LGPL-3.0 +CoreNLP|java|6.7k|9/9/!|是|是|是|是|是|否|否|否|否|否|GUN2.0 +fnlp|java|2.2k|6/0.9/!|是|是|是|是|是|是|是|否|否|否|LGPL-3.0 +NLPIR|java|2.5k|?/1/!|是|是|否|否|否|否|是|否|否|否|not open +sego|go|1.2k|6/1/!|是|是|否|否|否|否|是|否|否|否|Apache-2.0 +ltp|c++|2.3k|6/1/!|是|是|是|是|是|是|是|否|否|否|LGPL-3.0 +PaddleNLP|c++|3.4k|6/1/!|是|是|是|是|是|是|是|是|是|是|Apache-2.0 + + +##备注 +* 1.year中"6/3/*"表示"项目开始时间/最近更新时间/在维护";!表示不维护,超过一年不维护,不回复issiue则认为放弃; +* 2.其他功能 + * snownlp: 拼音转换,繁简转换,tf-idf计算,切句子 + * deepnlp: tensorflow1.4训练的各种模型 + * NLPIR: 检索,敏感信息,文档去重,编码转换 + * Ltp: 事件抽取,srl,时间抽取, + * HanLP: 人民日报2014分词,文本推荐(相似度),索引分词 + * ansj-seg: 比较混乱,主页没有调用说明,词典是个大杂烩 + * word: 词频统计、词性标注、同义标注、反义标注、拼音标注 + * ltp: 特征裁剪策略,语义角色标注 + * PaddleNLP: Paddle训练,以及基础包,enienr生成等各种任务 +* 3.更多的统计学习方法 + 摘要,情感识别(酸甜苦辣),新词发现,实体与关系抽取,领域分类,生成 + + +##分词算法 +* 1.jieba + * 1.1 基于前缀词典实现高效的词图扫描，生成句子中汉字所有可能成词情况所构成的有向无环图 (DAG) + * 1.2 采用了动态规划查找最大概率路径, 找出基于词频的最大切分组合 + * 1.3 对于未登录词，采用了基于汉字成词能力的 HMM 模型，使用了 Viterbi 算法 +* 2.THULAC,pkuseg,Jiagu,fastNLP + * 2.1 CRF(char,word,elmo,bert) + * 2.2 feature+CRF +* 3.ansj-seg + * 3.1 n-Gram+CRF+HMM +* 4.HanLP + * 4.1 n-Gram, CRF +* 5.sego + * 5.1 基于词频的最短路径加动态规划 +* 6.Ltp + * 6.1 bilstm+crf + * 6.2 英文、URI一类特殊词识别规则 + 利用空格等自然标注线索 + 在统计模型中融入词典信息 + 从大规模未标注数据中统计的字间互信息、上下文丰富程度 +* 7.PaddleNLP + * 7.1 gru+crf +* 8.word(最大匹配法、最大概率法、最短路径法) + * 8.1 正向最大匹配算法,逆向最大匹配算法,正向最小匹配算法,逆向最小匹配算法 + * 8.2 双向最大匹配算法,双向最小匹配算法,双向最大最小匹配算法 + * 8.3 全切分算法,最少词数算法,最大Ngram分值算法,最短路径法 + * 8.4 语义切分:扩充转移网络法、知识分词语义分析法、邻接约束法、综合匹配法、后缀分词法、特征词库法、矩阵约束法、语法分析法 + + +## 工具包地址 +* jiba:[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba) +* HanLP:[https://github.com/hankcs/HanLP](https://github.com/hankcs/HanLP) +* CoreNLP:[https://github.com/stanfordnlp/CoreNLP](https://github.com/stanfordnlp/CoreNLP) +* ansj-seg:[https://github.com/lionsoul2014/jcseg](https://github.com/lionsoul2014/jcseg) +* THULAC-Python:[https://github.com/thunlp/THULAC-Python](https://github.com/thunlp/THULAC-Python) +* pkuseg-python:[https://github.com/lancopku/pkuseg-python](https://github.com/lancopku/pkuseg-python) +* snownlp:[https://github.com/isnowfy/snownlp](https://github.com/isnowfy/snownlp) +* deepnlp:[https://github.com/rockingdingo/deepnlp](https://github.com/rockingdingo/deepnlp) +* fastNLP:[https://github.com/fastnlp/fastNLP](https://github.com/fastnlp/fastNLP) +* Jiagu:[https://github.com/ownthink/Jiagu](https://github.com/ownthink/Jiagu) +* xmnlp:[https://github.com/SeanLee97/xmnlp](https://github.com/SeanLee97/xmnlp) +* word:[https://github.com/ysc/word](https://github.com/ysc/word) +* jcseg:[https://github.com/lionsoul2014/jcseg](https://github.com/lionsoul2014/jcseg) +* paddleNLP:[https://github.com/PaddlePaddle/models](https://github.com/PaddlePaddle/models) +* sego:[https://github.com/huichen/sego](https://github.com/huichen/sego) +* ik-analyzer:[https://github.com/wks/ik-analyzer](https://github.com/wks/ik-analyzer) +* fnlp:[https://github.com/FudanNLP/fnlp](https://github.com/FudanNLP/fnlp) +* NLPIR:[https://github.com/NLPIR-team/NLPIR](https://github.com/NLPIR-team/NLPIR) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..7bceb44 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +# tensorflow-gpu==1.15.0, tensorflow==1.15.0 +scikit-learn>=0.19.1 +pandas>=0.23.4 +passlib>=1.7.1 +gensim>=3.7.1 +numpy>=1.16.2 +tqdm>=4.31.1 +keras-bert>=0.80.0 +keras-adaptive-softmax>=0.6.0 +regex diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..9a83d8e --- /dev/null +++ b/setup.py @@ -0,0 +1,67 @@ +# -*- coding: UTF-8 -*- +# !/usr/bin/python +# @time :2019/12/30 22:17 +# @author :Mo +# @function :setup of Macropodus +# @codes :fix it and copy reference from https://github.com/TianWenQAQ/Kashgari/blob/master/setup.py + + +from macropodus.version import __version__ +from setuptools import setup +import codecs + + +# Package meta-data. +NAME = 'Macropodus' +DESCRIPTION = 'Macropodus: Tookit of Chinese Natural Language Processing' +URL = 'https://github.com/yongzhuo/Macropodus' +EMAIL = '1903865025@qq.com' +AUTHOR = 'yongzhuo' +LICENSE = 'MIT' + +with codecs.open('README.md', 'r', 'utf8') as reader: + long_description = reader.read() +with codecs.open('requirements.txt', 'r', 'utf8') as reader: + install_requires = list(map(lambda x: x.strip(), reader.readlines())) + +setup(name=NAME, + version=__version__, + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + author_email=EMAIL, + url=URL, + packages=['macropodus'], + package_dir={'macropodus': 'macropodus'}, + package_data={'macropodus': ['*.*', 'data/*', 'data/dict/*', 'data/cache/*', + 'data/embedding/*', 'data/embedding/word2vec/*']}, + install_requires=install_requires, + license=LICENSE, + classifiers=['License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: Implementation :: CPython', + 'Programming Language :: Python :: Implementation :: PyPy'], + ) + + +if __name__ == "__main__": + print("setup ok!") + +# 说明, tensorflow>=1.14.0 or tensorflow-gpu>=1.14.0 +# 项目工程目录这里Macropodus, 实际上, 下边还要有一层macropodus, 也就是说, macropodus和setup同一层 +# data包里必须要有__init__.py, 否则文件不会生成, .py文件才能copy +# 编译的2种方案: + +# 方案一 +# 打开cmd +# 到达安装目录 +# python setup.py build +# python setup.py install + +# 方案二 +# python setup.py bdist_wheel --universal +# twine upload dist/* diff --git a/test/evaluate/__init__.py b/test/evaluate/__init__.py new file mode 100644 index 0000000..c231459 --- /dev/null +++ b/test/evaluate/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/17 10:38 +# @author : Mo +# @function: \ No newline at end of file diff --git a/test/evaluate/data/__init__.py b/test/evaluate/data/__init__.py new file mode 100644 index 0000000..8fa5386 --- /dev/null +++ b/test/evaluate/data/__init__.py @@ -0,0 +1,5 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/17 10:41 +# @author : Mo +# @function: \ No newline at end of file diff --git a/test/evaluate/data/ambiguity.txt b/test/evaluate/data/ambiguity.txt new file mode 100644 index 0000000..53ae78d --- /dev/null +++ b/test/evaluate/data/ambiguity.txt @@ -0,0 +1,52 @@ +工信处 女 干事 每月 经过 下属 科室 都要 亲口 交代 24 口 交换机 等 技术性 器件 的 安装 工作 +研究 生命科学 \t 研究 生命 科学 +研究生 命令 本科生 +我 从 马 上 下来 +我 马上 下来 +北京 大学生 喝 进口 红酒 +在 北京大学 生活区 喝 进口 红酒 +从小 学 电脑 +从 小学 毕业 +美军 中将 竟 公然 说 +新建 地铁 中 将 禁止 商业 摊点 +这块 地 面积 还真 不小 +地面 积了 厚厚 的 雪 +让 我们 以 爱心 和 平等 来 对待 动物 +阿美 首脑 会议 将 讨论 巴以 和平 等 问题 +锌 合金 把手 的 相关 求购 信息 +别 把 手 伸进 别人 的 口袋 里 +将 信息 技术 应用 于 教学 实践 +信息 技术 应用 于 教学 中 的 哪个 方面 +上级 解除 了 他 的 职务 +方程 的 解 除了 零 以外 还有 … +我们 一起 去 故宫 +一起 恶性 交通 事故 +我 不想 吃 东西 +你 就 不 想想 +各 国有 企业 相继 倒闭 +各国 有 各国 的 困难 +老人家 身体 不错 +老人 家中 很 干净 +和服 务必 归还 +技术 和 服务 +他 站 起 身 +他 起身 去 北京 +问题 的 确定 +这的 确定 不 下来 +结合 成分 +为 人民 工作 +中国 产品 质量 +原子 结合 成 分子 时 +部分 居民 生活 水平 +治理 解放 大道 路面 积水 +这样 的 人 才能 经受 住 考验 +他俩 儿 谈 恋爱 是 从 头年 元月 开始 的 +在 这些 企业 中 国有 企业 有 十个 +结婚 的 和 尚未 结婚 的 +热海 景区 +热海 景区 + +崔永元 炮轰 范冰冰 +这 源自 萧红 写给 萧军 信中 的 一句话 +阿里 大华 腾讯 百度 +亲家公 亲家母 +情侣 们 在 海南岛 上 海誓山盟 diff --git a/test/evaluate/tet_evaluate.py b/test/evaluate/tet_evaluate.py new file mode 100644 index 0000000..3f9338b --- /dev/null +++ b/test/evaluate/tet_evaluate.py @@ -0,0 +1,54 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/17 21:13 +# @author : Mo +# @function: test evulate + + +from macropodus.preprocess.tools_common import txt_write, txt_read +import macropodus +import time + + +def evulate_file(path_file): + """ + 验证切词的各种指标 + :param path_file: str, like '/train.txt' + :return: float + """ + # 读取数据 + sents = txt_read(path_file) + # 初始化统计计数 + count_macropodus = 0 + count_real = 0 + count_true = 0 + count = 0 + # 切词与统计, true + for sent in sents: + sent_sp = sent.strip() + res_real = sent_sp.split(' ') + sentence = sent_sp.replace(' ','') + res_macropodus = macropodus.cut(sentence) + print(res_macropodus) + count += 1 + count_real += len(res_real) + count_macropodus += len(res_macropodus) + for cm in res_macropodus: + if cm in res_real: + count_true += 1 + res_real.remove(cm) + # precision, recall, f1 + precision = count_true / count_macropodus + recall = count_true / count_real + f1 = (precision * recall * 2) / (precision + recall) + + return precision, recall, f1 + + +if __name__ == "__main__": + path_file = 'data/ambiguity.txt' + time_start = time.time() + precision, recall, f1 = evulate_file(path_file) + print('time: ' + str(time.time()-time_start)) + print('precision\t', 'recall\t', 'f1') + print(precision, recall, f1) diff --git a/test/evaluate/tet_macropodus.py b/test/evaluate/tet_macropodus.py new file mode 100644 index 0000000..75e82f8 --- /dev/null +++ b/test/evaluate/tet_macropodus.py @@ -0,0 +1,84 @@ +# !/usr/bin/python +# -*- coding: utf-8 -*- +# @time : 2019/12/17 21:13 +# @author : Mo +# @function: test macropodus + + +import time +time_start = time.time() +import macropodus +print('macropodus初始化耗时: ' + str(time.time()-time_start) + 's') + +# import sys +# import os +# print(os.name) +# print(sys.platform) + +# macropodus.load_user_dict(path_user="user.json", type_user="json") +macropodus.add_word(word="斗鱼属") +macropodus.add_word(word="斗鱼科") +macropodus.add_word(word="鲈形目") +macropodus.save_add_words(word_freqs={"喜斗":32, "护卵":64, "护幼":132}) +macropodus.add_word(word="坑爹的平衡性基金") +macropodus.save_add_words(word_freqs={"BBC":132}) + +print(macropodus.cut("坑爹的平衡性基金啊,坑爹呀斗鱼属，Macropodus (Lacépède, 1801)，鲈形目斗鱼科的一属鱼类。" + "本属鱼类通称斗鱼。因喜斗而得名。分布于亚洲东南部。中国有2种，即叉尾斗鱼，分布于长江及以南各省；" + "叉尾斗鱼，分布于辽河到珠江流域。其喜栖居于小溪、河沟、池塘、稻田等缓流或静水中。" + "雄鱼好斗，产卵期集草成巢，雄鱼口吐粘液泡沫，雌鱼产卵其中，卵浮性，受精卵在泡沫内孵化。雄鱼尚有护卵和护幼现象。" + )) + +sen_calculate = "23 + 13 * (25+(-9-2-5-2*3-6/3-40*4/(2-3)/5+6*3))加根号144你算得几多" +sen_chi2num = "三千零七十八亿三千零十五万零三百一十二点一九九四" +sen_num2chi = 1994.1994 +sent1 = "PageRank算法简介" +sent2 = "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。" +summary = "PageRank算法简介。" \ + "是上世纪90年代末提出的一种计算网页权重的算法! " \ + "当时，互联网技术突飞猛进，各种网页网站爆炸式增长。 " \ + "业界急需一种相对比较准确的网页重要性计算方法。 " \ + "是人们能够从海量互联网世界中找出自己需要的信息。 " \ + "百度百科如是介绍他的思想:PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。 " \ + "Google把从A页面到B页面的链接解释为A页面给B页面投票。 " \ + "Google根据投票来源甚至来源的来源，即链接到A页面的页面。 " \ + "和投票目标的等级来决定新的等级。简单的说， " \ + "一个高等级的页面可以使其他低等级页面的等级提升。 " \ + "具体说来就是，PageRank有两个基本思想，也可以说是假设。 " \ + "即数量假设：一个网页被越多的其他页面链接，就越重）。 " \ + "质量假设：一个网页越是被高质量的网页链接，就越重要。 " \ + "总的来说就是一句话，从全局角度考虑，获取重要的信。 " + +# 分词 +words = macropodus.cut(summary) +print(words) +new_words = macropodus.find(summary) +print(new_words) + +# 新词发现(findword, 默认接口) +sents = macropodus.find(text=summary, freq_min=2, len_max=7, entropy_min=1.2, aggregation_min=0.5, use_avg=True) +print(sents) +# 摘要 +sum = macropodus.summarize(summary) +print(sum) +keyword = macropodus.keyword(summary) +print(keyword) +# 相似度 +sim = macropodus.sim(sent1, sent2, type_sim="cosine") + +sent1 = "叉尾斗鱼" +sent2 = "中国斗鱼生性好斗,适应性强,能在恶劣的环境中生存" + +# 文本相似度(similarity) +sents = macropodus.sim(sent1, sent2, type_sim="total", type_encode="avg") +print(sents) +sents = macropodus.sim(sent1, sent2, type_sim="cosine", type_encode="single") +print(sents) +print(sim) +# tookit +score_calcul = macropodus.calculate(sen_calculate) +print(score_calcul) +res_chi2num = macropodus.chi2num(sen_chi2num) +print(res_chi2num) +res_num2chi = macropodus.num2chi(sen_num2chi) +print(res_num2chi)