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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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, "孪生": 1997, "哥德巴赫": 960, "系": 698267, "有理数": 3343, "子集": 4778, "实数": 8661, "连续": 178662, "是以": 104376, "进一步": 144275, "广义": 21002, "化成": 8085, "化": 303883, "四": 733655, "元数": 963, "八": 290546, "推广到": 2607, "超限": 4137, "另一个": 142763, "大小": 202433, "基数": 16904, "阿": 424412, "列": 170459, "夫": 205178, "允许": 152711, "比较": 1517362, "如数": 1616, "函数": 146363, "物件": 13185, "内含": 10769, "群": 228955, "环": 138962, "体": 272424, "该": 2214421, "些": 345665, "也就是": 206193, "这样": 1752444, "向量": 23455, "线性": 34583, "结合": 204765, "三个": 287854, "扩展": 125304, "第四个": 11375, "创立": 42905, "二十世纪": 9361, "三十年代": 1425, "法国": 250624, "布尔巴基": 161, "学派": 13910, "初始": 40298, "出发": 136694, "三种": 76316, "序": 41974, "偏序": 559, "全": 657946, "拓扑": 8974, "邻域": 1338, "极限": 62476, "连通": 6972, "维数": 1931, "包含有": 3767, "勾股": 2496, "推广": 97349, "更高": 76671, "维": 240478, "非": 489155, "几": 412055, "何其": 4391, "相对论": 13460, "拓扑学": 1277, "解析": 53026, "微分": 7181, "中都": 16896, "纤维": 59350, "丛": 29271, "流": 195819, "形上": 793, "多项式": 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"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": 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"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": 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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": 16680, "神": 434986, "主宰": 11062, "巴门尼德": 193, "芝诺": 502, "无": 1625366, "生有": 5267, "都是": 1815048, "爱利亚": 35, "不变": 58611, "赫拉克利特": 303, "强调": 103994, "才是": 192504, "真实": 208742, "永久": 46852, "外界": 49499, "宣传": 145235, "自己": 4516026, "一团": 10394, "熊熊": 29183, "燃烧": 68850, "火焰": 32455, "火": 349994, "更加": 231704, "暗示": 39013, "秩序": 54517, "多元论": 137, "恩培": 182, "克勒": 4061, "元素": 142267, "萨": 132039, "戈": 71630, "拉": 605875, "种子": 71751, "粒子": 29455, "德谟克利特": 224, "原子": 43493, "原子论": 343, "若干": 66395, "中期": 36786, "普罗泰戈拉": 115, "高尔": 2561, "吉亚": 2885, "辩": 22719, "士": 130966, "重点": 215764, "转移到": 18222, "人才": 133771, "本": 1071882, "真正": 326992, "是非": 26376, "善恶": 8135, "相对于": 36933, "感觉": 956921, "同样": 301721, "假": 226153, "怀疑论": 1010, "雏形": 4521, "世纪末": 11101, "主张": 65400, "前述": 13123, "又有": 82132, "记载": 85970, "第一个": 197828, "或者说": 55471, "就是说": 33264, "实质": 22491, "平均": 166430, "调和": 14681, "三大": 50372, "基调": 6381, "说明": 717303, "所在": 139200, "总结为": 1486, "十种": 2762, "相异": 1764, "有限": 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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"纳米": 23648, "哈伯": 1981, "超深": 349, "可见光": 4855, "影象": 1028, "恒星": 41891, "星系": 24484, "星云": 11420, "星": 337047, "姊妹": 13643, "简略": 4160, "光度": 6496, "类星体": 1207, "致密": 3836, "双星": 5868, "迈斯纳": 145, "磁铁": 8618, "悬浮": 9897, "超导体": 1322, "上方": 40785, "突破性": 3153, "划时代": 2539, "夜以继日": 871, "如火如荼": 2549, "地核": 839, "比喻为": 1358, "发电机": 18958, "不停": 69926, "对流": 7639, "电流": 81691, "柯氏力": 19, "地磁": 1328, "翻转": 8184, "上次": 52179, "间隔": 32228, "历来": 10051, "长久": 29981, "高于": 68932, "导电性": 1856, "高温": 58372, "电子学": 1724, "征象": 2181, "瞩目": 6130, "缺失": 24862, "途中": 67676, "转换为": 13405, "侦测": 5088, "别": 563147, "带": 785191, "辨明": 612, "震荡": 14718, "到处": 55071, "反物质": 1629, "惰性": 3628, "中微子": 2999, "蛛丝马迹": 2010, "对撞": 941, "暗物质": 3035, "衰变": 4514, "管道": 56043, "大半个世纪": 131, "合意": 1825, "超高": 16413, "宇宙射线": 1122, "重子": 1785, "不对称性": 525, "自转": 8691, "列出": 32158, "微波": 13715, "浩瀚": 4023, "星空": 23277, "外行星": 3084, "孕育": 8010, "量子物理": 981, "湍流": 2162, "一知半解": 2259, "地震": 72900, "断裂": 32277, "很远": 16201, "些许": 7528, "逖": 420, "物理学奖": 2533, "档案馆": 3920, "网页": 116680, "相片": 22646, "刊物": 10596, "加州": 28023, "理工学院": 8793, "影音": 13587, "杰": 170270, "胡夫": 651, "金石": 6152, "良言": 548, "蟹": 43709, "状": 129466, "超新星": 5594, "哈勃": 2788, "太空": 162277, "摄": 26335, "天体": 26805, "卫星": 69293, "彗星": 12477, "爆炸": 70679, "伽玛": 2201, "射线": 11671, "远古": 11847, "巴比伦": 6138, "努比亚": 5576, "伊朗": 42815, "夜空": 5735, "测量学": 955, "航海": 16249, "历法": 5428, "同义": 2408, "业余": 33151, "天文学家": 14021, "肉眼": 10836, "还能": 190842, "日期": 110879, "时长": 16766, "中美洲": 4405, "天文台": 45958, "思索": 6667, "天上": 37889, "萌发": 5803, "日月": 16135, "日月星辰": 796, "天象": 2000, "奠下": 334, "月食": 4632, "沙": 230579, "赤道": 11275, "日晷": 1434, "阿富汗": 22559, "出土": 14573, "接著": 10272, "阿里": 45830, "斯塔克": 2349, "太阳系": 14441, "喜帕恰斯": 155, "岁差": 552, "月球": 43422, "仪器": 40860, "星盘": 1124, "含": 205785, "颗": 199193, "提": 264097, "基特": 117950, "一日": 38995, "钟": 149782, "停滞不前": 2158, "中古": 4799, "仙女座": 1935, "中视": 9202, "星等": 9629, "巴塔尼": 118, "比特": 9500, "鲁尼": 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15202, "孙科": 963, "首届": 12652, "第一名": 25999, "总督": 22035, "臣": 45662, "亲自": 55024, "镜湖": 6266, "仁慈": 5043, "租": 78894, "议事": 3258, "亭": 66620, "十四号": 492, "医馆": 1286, "葡文": 714, "学识": 7867, "精明": 7479, "名师": 10562, "游": 178023, "洞": 83822, "窥秘": 23, "赠": 36464, "尚有": 15356, "诊症": 105, "余闲": 43, "不欲": 1768, "酌定": 10753, "计较": 13299, "情致": 491, "厘订": 62, "规条": 392, "著明": 110, "刻": 37684, "候": 36662, "十点钟": 1473, "十二点钟": 148, "分文": 1369, "贫乏": 2979, "一点钟": 1018, "三点钟": 1397, "写字楼": 9418, "候诊": 453, "出门": 73505, "就诊": 22968, "所订": 461, "俱": 24571, "减": 97828, "订": 39589, "款": 652193, "其人": 8605, "奢求": 1704, "务": 20511, "祈": 7762, "相与": 2950, "有成": 2190, "济人": 247, "中西药": 811, "药局": 910, "掩护": 14003, "孙中山像": 95, "乡间": 4585, "草拟": 1965, "万言书": 248, "大经": 1015, "治国": 6007, "大本": 3289, "物能": 368, "货": 138765, "畅": 37251, "亲赴": 796, "呈给": 245, "直隶总督": 853, "长女": 5961, "月中": 7775, "偕": 3197, "旋": 32177, "游历": 4256, "开打": 3896, "请愿书": 1049, "刊载": 3891, "公报": 4548, "逗留": 5627, "夏末": 1822, "乘船": 3772, "欧胡岛": 254, "募款": 1649, "涉": 18594, "华籍": 233, "经理": 129010, "召集": 17792, "侨社": 125, "昌": 95892, "刘祥": 296, "程蔚": 10, "荫": 12926, "金黄": 11837, "亮": 145706, "黄华": 1664, "恢": 5113, "卓": 62945, "李禄": 67, "马林": 3088, "鉴泉": 226, "刘": 1146384, "工": 106911, "仁": 102848, "侯艾": 11, "振兴中华": 558, "合众": 1145, "政纲": 799, "誓词": 1473, "和泰": 2015, "商号": 1325, "同志": 48290, "筹谋": 327, "纠集": 43765, "故交": 446, "同窗": 2660, "衢": 7962, "谢": 438731, "缵": 1045, "辅仁": 743, "文社": 1773, "领导权": 1216, "抱负": 5408, "自信": 80474, "旗鼓相当": 996, "亨": 28667, "中环": 16002, "号外": 1840, "悬": 25840, "招牌": 13553, "咏": 32912, "胜任": 11370, "议政": 1755, "大律师": 3013, "戚": 24276, "谊": 4477, "相识": 23261, "入会": 2455, "干部": 71888, "攻取": 1919, "起义军": 3279, "之秋": 1082, "波及": 5856, "驱逐出": 1143, "广征": 140, "重阳节": 4526, "人事案": 102, "延宕": 618, "孙杨": 5183, "各人": 8982, "对峙": 9950, "剑拔弩张": 834, "职位": 65926, "云孙": 223, "指挥权": 1416, "与会者": 2362, "为誓": 173, "倘": 5897, "贰": 10046, "心神": 2408, "明鉴": 900, "云为": 1422, "会办": 636, "玺": 19408, "天德": 1563, "音译": 10726, "秘书": 26414, "月初": 87488, "农学会": 58, "清晨": 25841, "各路": 12403, "人马": 11317, "首领": 20580, "家祠": 528, "讨": 18957, "口令": 9385, "待命": 2543, "码头": 47309, "迎接": 20689, "点收": 186, "军火": 4753, "佣兵": 5566, "时许": 1805163, "接": 279212, "电报": 8145, "有线电报": 46, "事先": 96530, "泄密": 3318, "汕头": 12190, "募集": 11508, "进逼": 929, "期于": 526, "某日": 33781, "进发": 2853, "登上": 31287, "驻防": 2020, "逮个正著": 33, "不料": 7523, "忽有": 549, "密电": 605, "驰": 25976, "中途": 23436, "阻": 26749, "进": 395994, "应援": 5575, "势": 51143, "孤": 42413, "起事": 2492, "万全": 1386, "延迟": 31145, "解散": 25480, "处刑": 8275, "即便": 41427, "严刑拷打": 449, "拒不": 45121, "供出": 930, "同谋": 1060, "赴义": 90, "决不": 8100, "光复": 5009, "杀": 245513, "港府": 3317, "放逐": 4398, "不准": 35316, "坐轿": 178, "逃": 42617, "广岛": 7311, "驶往": 28802, "分会": 7806, "横滨": 8126, "神户": 6387, "随行": 4354, "遇有": 3507, "翌日": 6878, "支那": 3848, "革命党": 1552, "蛰伏": 1151, "外型": 9809, "剪掉": 6305, "辫子": 4598, "薙发": 106, "脱下": 6591, "长袍": 2978, "换穿": 208, "欧式": 9641, "西服": 8941, "策划": 48860, "筹集": 11719, "转汇": 367, "购": 65874, "转运": 14031, "避难": 5793, "东京": 89602, "旋即": 2986, "妻小": 311, "重逢": 6603, "在港": 3146, "安定": 19742, "签发": 11301, "遂令": 566, "年内": 54955, "踏足": 1172, "蒙难": 315, "逃亡": 19124, "浮雕": 5755, "坐船": 3122, "雇": 6457, "马车": 10397, "胥": 13648, "旅社": 9708, "投宿": 807, "子夜": 2931, "昔日": 15138, "恩师": 3469, "覃": 73456, "晤见": 25, "康氏": 400, "伉俪": 1150, "公使": 3565, "龚": 92329, "诱捕": 1549, "萧若元": 406, "黄宇": 299, "使馆": 9058, "游荡": 4400, "领": 128612, "领馆": 1749, "相熟": 1199, "第二天": 95504, "工作人员": 190489, "得悉": 1800, "前来": 44777, "埋伏": 5758, "拘捕": 5159, "遣送回": 570, "法律制裁": 1433, "公使馆": 1051, "仆人": 6841, "柯尔": 2907, "比作": 5254, "土耳其人": 2580, "亚美尼亚": 9338, "现金": 657798, "允诺": 2482, "事后": 69417, "便条": 853, "上头": 6176, "星期日": 13236, "始": 54965, "施以": 3638, "诱骗": 7312, "骤": 3227, "幽禁": 740, "中国使馆": 247, "一二": 18534, "回国": 44225, "斩首": 5099, "奈何": 8954, "环球报": 320, "英国外交部": 340, "无权": 9107, "此事": 38256, "脱险": 2038, "投稿": 9942, "记者会": 7637, "嬴得": 215, "遍": 64097, "剑桥大学": 6632, "汉学家": 1381, "翟": 45854, "大英博物馆": 1306, "民党": 351, "领袖": 43965, "犬养": 499, "毅": 26188, "宫崎寅": 28, "平山": 4835, "相会": 7281, "一见如故": 811, "快慰": 272, "周某": 365792, "寄": 74163, "旅馆": 33225, "鹤": 55518, "馆": 79489, "住宿": 41416, "日本式": 494, "想起": 81604, "日前": 46996, "侯爵": 3465, "邸": 7689, "帮": 664541, "填": 72621, "姓": 89809, "写上": 10886, "广交": 459, "羽翼": 3558, "为伴": 1476, "结识": 16061, "进步党": 938, "隈": 2806, "重信": 458, "黑龙": 7531, "山田": 9462, "取道": 1568, "炳": 14313, "女仆": 6891, "浅": 69357, "田春": 336, "快要": 19724, "被迫": 46515, "女佣": 1811, "静冈县": 2712, "懂": 156780, "行止": 945, "患病": 16016, "为由": 167011, "静冈": 2322, "老家": 37899, "伤心": 60984, "合照": 8539, "安永": 1324, "助": 62055, "小山": 7950, "雄": 106308, "藤": 61915, "七郎": 1206, "辗转": 7327, "春天": 75619, "会合": 9027, "春夏": 7400, "来往于": 634, "长崎": 6259, "马关": 1766, "里亚": 16491, "彭": 147521, "秋冬": 12666, "亡命": 2431, "策动": 1678, "军购": 401, "军械": 2349, "中国日报": 1256, "襄助": 280, "尔后": 19028, "靠山": 3230, "殖民地": 23404, "立法局": 2722, "韦玉": 64, "无可争辩": 385, "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": 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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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"旗面": 529, "红黄": 3791, "蓝白": 3036, "五色": 4807, "横条": 998, "满蒙": 941, "选用": 57405, "指使": 34448, "手下": 20814, "段祺瑞": 2114, "将领": 22855, "联名": 7273, "通电": 12752, "共和制": 1258, "拥护": 7428, "君主立宪制": 1504, "主战": 5032, "高涨": 7005, "用兵": 3483, "北伐军": 1177, "海陆军": 201, "自任": 1284, "总指挥": 4111, "参谋长": 11174, "津浦路": 259, "孙岳": 171, "淮": 27634, "柏文蔚": 153, "军长": 7200, "蓝天": 17086, "蔚为": 303, "第二军": 672, "大本营": 20012, "下旬": 42586, "首战": 2386, "告捷": 371, "粤军": 821, "蚌埠": 8328, "固镇": 1597, "宿州": 3478, "清军": 8110, "张勋": 1196, "望风而逃": 119, "徐州": 24661, "奋勇": 2094, "传来": 14084, "捷报": 1056, "进抵": 400, "烟台": 16676, "沪": 55193, "震动": 19885, "恐慌": 12237, "民军": 496, "秦皇岛": 8058, "增兵": 1070, "军费": 4963, "匮乏": 8017, "粮草": 2045, "饷": 3708, "械": 4548, "奇缺": 577, "时值": 2724, "严冬": 1651, "御寒衣": 143, "冻死": 3985, "士气": 8028, "大受": 5862, "六路": 3773, "名义上": 6543, "各自为战": 441, "进军": 16343, "受阻": 7207, "财政危机": 619, "举步维艰": 1187, "张謇": 673, "汤寿潜": 58, "德全": 602, "议和": 2231, "清帝": 707, "退位": 4409, "不敢": 156459, "胡作非为": 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13359, "依赖性": 7449, "既使": 1175, "面上": 9154, "每每": 9113, "自订": 895, "无误": 8593, "切合": 3447, "察觉": 10872, "支承": 2463, "程序库": 550, "包装成": 1392, "部件": 35744, "守护": 37146, "此列": 1056, "绝非": 6205, "四海皆准": 179, "视窗": 3869, "分野": 1083, "实时": 40471, "构件": 12169, "超微": 2111, "混居": 412, "产生出来": 575, "功能性": 11922, "理论界": 849, "存有": 11478, "家族史": 2468, "上溯": 1352, "工程量": 7545, "繁重": 4757, "据有": 671, "高性能": 12446, "同义词": 3596, "预知": 5934, "如果说": 41326, "音频": 36369, "或缺": 621, "中专": 16019, "认可为": 509, "汇流": 2505, "宽度": 29681, "定长": 466, "同样道理": 849, "太小": 35092, "二维": 11574, "置换": 13140, "绰绰有余": 2250, "一维": 4003, "俗称": 86848, "移除": 10434, "讲解": 37166, "处理机": 917, "集群": 10550, "梅隆": 979, "麒麟": 23623, "康柏": 791, "西门子": 9048, "西门子公司": 627, "远远": 49739, "大型计算机": 300, "电子游戏机": 4197, "被打": 26902, "包成": 1127, "办公": 61611, "押韵": 3948, "音标": 10060, "读法": 4291, "道格拉斯": 5841, "麦克": 29481, "乔伊": 3313, "更佳": 9482, "进度": 36383, "缓慢": 38539, "轻量": 1457, "上学": 76123, "管理员": 42167, "改成": 46891, "程式码": 1573, "开机": 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"踏踏实实": 5256, "写好": 12813, "整理好": 3504, "落伍": 3095, "陈旧": 7226, "较差": 18155, "预料": 7827, "开发出来": 2250, "牛羚": 298, "头像": 42014, "奴": 27656, "附带": 120371, "应为": 16625, "孵化": 10491, "排版": 21909, "源起": 840, "无偿": 9159, "兼并": 5799, "开发出": 6605, "强健": 5629, "全功能": 1515, "简要": 13921, "略胜一筹": 874, "专利权": 4973, "自由化": 2289, "无益": 4047, "技术革新": 1195, "控制权": 6250, "副产物": 1246, "倡议者": 200, "圣荷西": 1064, "州立": 9008, "纵使": 4654, "唤醒": 15417, "坚信": 12539, "说明书": 36732, "倡议": 7832, "支持者们": 654, "开发商": 45946, "邪恶": 24945, "犯罪人": 1378, "玛格丽特": 5442, "在在": 8540, "加州大学": 6814, "立足于": 2854, "选择性": 16780, "艾略特": 1816, "同伙": 11495, "著作权": 16427, "委内瑞拉": 8995, "法令": 11429, "出力": 4555, "总经理": 44792, "封信": 13114, "德加": 2515, "维拉": 12017, "州级": 846, "仓库": 70248, "承受": 52626, "回返": 452, "化学工程": 2015, "机械工程": 3413, "土木工程": 8065, "鉴识": 745, "指标性": 398, "前景": 59302, "青铜器时代": 313, "冶金学": 341, "吉布斯": 1493, "塑胶": 11388, "计划署": 593, "聚合物": 11242, "科技史": 320, "金属材料": 5589, "高强度": 9815, "耐": 62950, "导电": 8129, "绝缘": 17478, "单晶": 1444, "多晶": 617, "晶态": 566, "压电": 2613, "热电": 3694, "声光": 1407, "建筑材料": 6952, "研磨": 6477, "耐火材料": 1570, "耐酸": 1463, "电工": 15705, "感光材料": 358, "包装": 155915, "冶金": 9385, "粉末": 34659, "薄膜": 14520, "改性": 6429, "涂": 92050, "热处理": 5348, "宇航": 3448, "储": 31333, "科学奖": 705, "终生": 14818, "子嗣": 2835, "亲兄弟": 2747, "炸药": 15902, "科研成果": 2514, "和平主义": 964, "痛心": 5082, "辞世": 2238, "变做": 375, "前一年": 3479, "国际性": 5033, "增设": 15678, "捐助": 5610, "评选": 18636, "寄出": 3548, "邀请函": 3987, "明文规定": 3295, "提名者": 452, "谣言": 15518, "和平奖": 2668, "突": 51684, "尼西亚": 5175, "四方集团": 70, "表列": 572, "小数点": 5635, "舍去": 1671, "出生地": 9092, "高校": 89648, "总人数": 3757, "换算": 8966, "居里夫人": 1493, "玛丽": 28581, "居里": 1822, "次数": 72589, "法国籍": 433, "裔": 15027, "玛丽亚": 7553, "沃": 68247, "斯卡": 11691, "钋": 612, "丈夫": 108721, "贝克勒": 107, "提炼出": 1556, "镭": 2569, "核武": 3041, "巴丁": 1392, "晶体管": 5333, "桑格": 776, "红十字": 4565, "竭尽全力": 2758, "战俘": 5486, "红十字会": 6752, "难民": 18522, "高级专员": 803, "大女儿": 3529, "约里奥": 202, "小女儿": 3911, "领取": 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"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, 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"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": 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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, "马尔可夫": 900, "辨认": 498263, "拼字": 828, "助益": 2511, "聊天室": 3623, "推特": 6730, "推文": 1520, "分群": 400, "标示": 14359, "语种": 8247, "说话人": 1189, "问答": 304426, "录好": 661, "语意": 2055, "声道": 6849, "制作出": 3919, "预录": 457, "字句": 3160, "正弦": 4819, "报读": 2254, "工研院": 334, "偏重": 6834, "中英文": 6705, "变声": 2800, "笔记本": 86344, "声调": 9093, "剖析器": 123, "罗马字": 1729, "读本": 4911, "语调": 5263, "导游": 19541, "余音": 1293, "手写": 11652, "初音": 5623, "二次大战": 3733, "整场": 4233, "轴心国": 2350, "参战国": 291, "中日军": 275, "军民": 7959, "细菌战": 632, "互不侵犯": 864, "国协": 714, "撕毁": 2279, "约定": 154009, "统辖": 1264, "落于": 1428, "中南半岛": 1755, "战区": 11677, "主导权": 1206, "斯大林格勒": 1683, "停下": 12731, "西西里岛": 1916, "库尔斯克": 948, "广岛市": 980, "跟进": 10161, "停息": 1382, "七七": 5035, "埃塞俄比亚": 6453, "索比": 1478, "欧战": 1573, "那天": 79263, "那年": 57712, "协约国": 2190, "罗马尼亚": 16970, "鄂图曼帝国": 1166, "布尔什维克": 3172, "尼古拉二世": 713, "跟著": 4598, "撤除": 1464, "参谋部": 1160, "渐趋": 1473, "魏玛共和国": 485, "投靠": 7770, "民族主义者": 2181, "墨索里尼": 1809, "极权主义": 1014, "代议制": 412, "世界大国": 424, "纳粹党": 2470, "纵火案": 947, "提拔": 9332, "塑造成": 1830, "极权": 1418, "国际联盟": 1695, "挑衅": 10005, "热河": 2830, "塘沽": 6711, "满州": 725, "绥远": 1964, "党代会": 1398, "魏玛": 1045, "告结": 28, "阵线": 6835, "征兵": 5100, "苏法": 84, "英德": 6270, "罗加诺": 117, "莱茵兰": 4935, "佛朗哥": 1112, "轴心": 2664, "停火": 4010, "阿比西尼亚": 334, "简短": 10098, "厄立特里亚": 1309, "第十条": 4756, "格尔尼卡": 201, "弗朗西斯": 5329, "代理人": 30352, "秃鹰": 2483, "军团": 30739, "居住区": 2875, "独裁者": 2776, "东线": 6200, "借道": 1795, "安东尼奥": 3696, "萨拉": 11046, "青年团": 3807, "黑衫": 173, "七月底": 428, "军需": 2519, "后见": 2270, "素质": 89528, "逼使": 831, "后退": 19671, "缴械": 1422, "向前": 50493, "鼓": 52184, "移动到": 8758, "关东军": 3056, "深信": 3471, "南太平洋": 3335, "经验丰富": 8801, "诺维奇": 3907, "内维尔": 1522, "张伯伦": 2728, "达拉第": 89, "佐": 71221, "齐亚": 3673, "这件": 100568, "索赔": 10845, "苏台德": 403, "纳为": 999, "不久之后": 3624, "波希米亚": 2365, "摩拉维亚": 1023, "保护国": 1047, "震惊": 25974, "但泽": 779, "阿尔巴尼亚": 7762, "里宾特洛甫": 266, "苏德": 2347, "身后": 31981, "莫洛托夫": 569, "达成了": 25884, "立陶宛": 9269, "爱沙尼亚": 6127, "拉脱维亚": 4909, "比萨拉比亚": 238, "月末": 12038, "伯爵": 18253, "中立国": 943, "听说": 132614, "走廊": 23123, "国防军": 4049, "推开": 12543, "障碍物": 5199, "外海": 3206, "战况": 3110, "日苏": 871, "落入": 9665, "波罗的海": 5658, "中国军": 398, "比得哥什": 170, "坦克": 68621, "友好合作": 1532, "近乎": 10583, "剔除": 6556, "威尔": 19464, "弗瑞": 1813, "运往": 6707, "抢先": 6789, "冰岛": 12065, "温斯顿": 2234, "卢森堡": 6490, "闪电战": 986, "设下": 2352, "马奇诺防线": 303, "法军": 6470, "装甲车": 5974, "阿登": 2025, "侧翼": 2181, "初时": 3143, "敦刻尔克": 1739, "占领区": 2070, "琪": 47532, "停泊": 5155, "阿尔及利亚": 6458, "争议性": 2508, "撒拉": 1575, "海狮": 2045, "马尔他": 1086, "马利": 5460, "新方案": 842, "现购": 96, "自运": 156, "美国海军": 14581, "铁钢": 191, "零件": 31202, "封存": 8114, "同盟条约": 495, "租借": 9179, "安全区": 2020, "大英帝国": 3121, "对对": 6170, "逼到": 2488, "光采": 381, "塔兰托": 448, "战列舰": 9147, "受损": 141342, "马塔": 1942, "伞兵": 3106, "运输机": 6997, "空降到": 185, "克里特岛": 1281, "推进到": 871, "战斧": 5126, "保加利亚": 12796, "克罗地亚": 9373, "维希": 1594, "托管地": 424, "空军基地": 5974, "击沉": 6085, "旗舰": 25295, "空战": 6764, "空袭": 9893, "互有": 2310, "施加": 8917, "三光": 1445, "游击队": 18272, "东南亚地区": 1601, "苏日": 317, "步兵": 25806, "巴巴罗萨": 809, "阿斯特拉罕": 585, "尔斯": 19947, "战线": 8103, "里海": 3052, "白海": 456, "驱赶": 4941, "夏日": 26123, "集团军": 13151, "装甲": 30388, "列宁格勒": 3049, "基辅": 7778, "歼灭": 6349, "空军部队": 616, "油田": 10621, "宪章": 4043, "塞瓦斯": 1088, "托波尔": 1123, "调往": 2493, "疲惫不堪": 1529, "暂缓": 3406, "闪击战": 346, "尔格": 2488, "间谍": 18239, "警戒": 8801, "施展": 7120, "荷属": 1866, "东印度": 1704, "移交给": 3769, "逐一": 8197, "亚利桑那": 2057, "太平洋舰队": 2052, "突袭": 9204, "延伸至": 6147, "从一开始": 11060, "歼灭性": 53, "偷袭": 9227, "申明": 4730, "丘吉尔": 3591, "伤亡者": 176, "囚犯": 6953, "科雷": 2371, "逼": 181766, "自治领": 887, "爪哇海": 204, "海军基地": 2177, "取下": 11257, "运补": 222, "加查": 287, "下一波": 737, "珊瑚": 17021, "中途岛": 2363, "引诱": 11515, "阿拉斯加": 8509, "阿留申群岛": 639, "削减": 11668, "巴布": 6442, "对位": 3221, "索罗门": 486, "瓜": 61890, "康纳尔": 257, "船舰": 2453, "特种部队": 8832, "骚扰": 22057, "战果": 3414, "高加索": 4157, "库班": 952, "沙地": 3416, "顿河": 1015, "东南侧": 2287, "窝": 38630, "苦战": 2724, "往回": 4125, 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"谭震林": 402, "邓子恢": 317, "张鼎丞": 169, "授衔": 926, "八分之一": 1161, "十四个": 1371, "格鲁吉亚": 6616, "阿塞拜疆": 4616, "加里宁格勒": 659, "格人": 329, "东正教会": 579, "公国": 6499, "大公": 12054, "自诩": 2571, "彼得一世": 454, "送入": 6416, "拥核": 418, "欧安组织": 126, "世贸组织": 1735, "独联体": 1099, "斡": 2124, "罗思": 552, "罗刹": 2007, "魔怪": 871, "四邻": 1032, "不雅": 3668, "遗诏": 1074, "罗尔": 4041, "惯于": 588, "露西亚": 739, "语助": 47, "俄联邦": 566, "诺曼人": 679, "奥列格": 467, "拉维奇": 1571, "下放": 4380, "不振": 1502, "农业国": 541, "农业集体化": 196, "病入膏肓": 856, "最高苏维埃": 1067, "从此之后": 1235, "圣彼得堡": 5606, "彼得格勒": 498, "梅德韦杰夫": 1498, "连选连任": 279, "国家杜马": 553, "安全会议": 620, "两院制": 563, "下议院": 4599, "上议院": 2409, "联邦委员会": 337, "各派": 1869, "紧急状态": 1933, "检查员": 970, "民防": 967, "贸易部": 531, "教育科学部": 19, "通讯部": 166, "自然资源部": 191, "管区": 2809, "分立出": 39, "联邦区": 292, "伏尔加格勒": 1043, "伏尔加河": 1604, "叶卡捷琳堡": 558, "该城": 2478, "新西伯利亚": 758, "鄂毕河": 336, "科学城": 813, "四岛": 804, "陆缘海": 71, "亚速海": 400, "巴伦支海": 407, "拉普": 2674, "楚科奇海": 116, "鄂霍次克海": 732, "阿穆尔": 819, "拉多加": 296, "北温带": 614, "月平均": 2380, "苔原": 557, "燃气轮机": 1839, "核设施": 866, "经常帐": 61, "裁员": 6570, "恶性循环": 6830, "叶利钦": 1869, "疲软": 3874, "乏力": 14264, "通胀率": 839, "综述": 11284, "运输量": 587, "变暖": 5448, "海冰": 1216, "摩尔曼斯克": 623, "拉迪": 4727, "沃斯托克": 902, "梅杰": 492, "俄国人": 2578, "装束": 2509, "聚积": 838, "鞑靼人": 1257, "摩尔多瓦": 2579, "穆尔": 3418, "中极": 673, "救世主": 4444, "牧首": 1130, "座堂": 2281, "瓦西里": 2370, "莫斯科红场": 227, "萨满": 3659, "腾格里": 447, "亚历山大城": 269, "耶路撒冷": 10149, "基里尔": 330, "方济": 2506, "合力": 11847, "大作家": 578, "托尔斯泰": 2821, "含混": 550, "谢尔盖": 2727, "丘克": 627, "乌扎拉": 42, "眼泪": 46742, "尼基塔": 668, "米哈尔科夫": 94, "米哈伊": 2981, "佐夫": 2151, "金棕榈奖": 859, "米哈": 611, "洛维": 3364, "玛莎": 2990, "集有": 1290, "预告片": 12830, "公关": 17989, "拖车": 6185, "记录簿": 211, "教具": 2215, "推估": 451, "受教权": 58, "莎拉波娃": 932, "夺金": 1403, "艺术体操": 920, "蹦床": 1157, "花样游泳": 1071, "赛艇": 2797, "训练场": 2223, "上佳": 1897, "优秀选": 1159, "霍尔金娜": 114, "涅莫夫": 50, "卡巴": 4547, "耶娃": 1113, "俄罗斯队": 644, "申办": 14021, "卡斯帕罗夫": 333, "卡尔波夫": 211, "军兵种": 775, "主帅": 9474, "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, 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"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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"尤利乌斯": 603, "卢格": 273, "水疗": 4211, "凯尔特": 3413, "屡屡": 4976, "君士坦丁": 2135, "汪达尔": 450, "苏维汇": 80, "克洛维": 636, "世俗性": 90, "撒克逊": 1989, "吞": 19370, "布列塔尼": 2222, "烙印": 3899, "征服者": 3046, "冠以": 4807, "罗曼语": 806, "北部人": 47, "奥尔良": 2433, "原主": 638, "马特": 10871, "功勋卓著": 295, "图尔": 6733, "矮子": 3405, "卡洛": 5142, "查理曼": 1009, "宗良": 235, "功业": 1768, "加洛林": 468, "泰尔": 8482, "秃头": 2406, "维京人": 1541, "黑斯廷斯": 606, "诺曼底": 3420, "公爵": 21476, "圣女": 3952, "贞德": 2617, "绝嗣": 727, "走到": 68914, "尽头": 17299, "王座": 5810, "两百": 12256, "骑士": 102925, "对垒": 1674, "主力军": 2076, "编织": 10545, "异端": 3960, "图卢兹": 2504, "着力于": 548, "神官": 1364, "越发": 8527, "膝下": 1477, "无嗣": 1001, "萨利": 2383, "继承法": 2809, "伊莎贝": 2198, "旷日持久": 877, "黑死病": 1023, "肆虐": 3750, "凌厉": 1783, "乱局": 569, "南特": 1922, "上层社会": 1035, "停歇": 2319, "臭名昭著": 1994, "纳瓦拉": 1253, "波旁王朝": 798, "身影": 13977, "萨米": 1662, "德尚": 691, "普兰": 3059, "黎塞留": 542, "路易十四": 2101, "安妮": 17680, "儒勒": 877, "马萨": 2184, "投石": 1791, "凡尔赛宫": 1015, "路易十五": 635, "洛林": 2625, "逊色": 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"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, 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"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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"仞": 1955, "通到": 1228, "修造": 1545, "三辅": 524, "旧事": 3024, "兵刃": 757, "藏身": 2938, "手工作坊": 389, "刑满释放": 36450, "作工": 484, "渭北": 607, "三万家": 19, "骊": 2911, "当年冬天": 84, "汹涌": 3376, "考古所": 156, "地基": 12852, "清波": 3046, "美玉": 2522, "风水宝地": 650, "凿穿": 185, "椁": 957, "墓内": 255, "器皿": 5953, "人鱼": 8309, "鲇": 1913, "四脚": 946, "广志": 712, "娃娃鱼": 1128, "三秦记": 21, "明亮": 23571, "不灭": 6063, "盗墓者": 744, "水银": 6138, "挥发": 10825, "入葬": 328, "随葬品": 400, "剧毒": 5406, "地宫": 2620, "好像": 291584, "殉葬": 997, "盗墓": 13077, "不待": 3916, "墓门": 268, "秦陵": 560, "骸骨": 1757, "揭秘": 13347, "听后": 6314, "物探": 753, "土堆": 1683, "地平面": 393, "宫墙": 974, "南墙": 2111, "北墙": 920, "告破": 557, "防水性": 803, "难以想象": 3512, "水淹": 1871, "挡住": 13179, "从商": 1542, "墓道": 1149, "封土": 596, "取土": 1810, "沙石": 3297, "纯净": 19073, "陵陵区": 42, "挖来": 614, "深达": 1721, "刘士毅": 26, "江海": 4650, "动土": 2172, "口子": 7725, "戍卫": 115, "临潼": 1739, "杨志发": 25, "二百三十": 218, "六十二": 1224, "七千多": 534, "损及": 1293, "一无所获": 1489, "面东": 79, "水泽": 917, "卢生": 299, "预言书": 239, "居无定所": 568, "轻装": 2235, "无利可图": 569, "疲惫": 13406, "君父": 224, "自许": 730, "杨翁": 24, "秦皇": 2029, "榆中": 2706, "四十四个": 88, "抵挡不住": 825, "弩箭": 509, "穿透力": 2262, "对马": 3111, "骑手": 2698, "装配": 14352, "不公": 6769, "狱吏": 234, "拚命": 603, "军役": 189, "食粮": 843, "日以继夜": 420, "帐幕": 553, "孤儿寡妇": 103, "老弱病残者": 46, "倒在": 22553, "一鳞半爪": 88, "盖因": 525, "卢绾": 227, "东垣": 211, "黥": 569, "入伍": 10507, "五岭": 1012, "瘴疠": 278, "可想而知": 8753, "半生": 3323, "自言": 1738, "平民百姓": 1585, "平越": 182, "粗通": 159, "犹有余悸": 19, "想像到": 172, "看中": 28610, "珠玑": 2000, "甲兵": 959, "楼船": 737, "矛盾重重": 491, "有生力量": 678, "水土不服": 2088, "疫": 3485, "非战斗": 1368, "吁宋": 15, "夜袭": 1786, "僵持": 2543, "百战百胜": 955, "疑忌": 133, "靖州": 2901, "县境": 1553, "嶷": 1402, "湖南宁远": 78, "结余": 3089, "三至五年": 851, "良策": 932, "丛林战": 170, "见效": 15109, "五六年": 2886, "初见成效": 456, "中县": 255, "惰民": 28, "逋": 1023, "佗": 1452, "龙川": 4464, "被称": 2238, "张冠李戴": 581, "已极": 293, "缝补": 668, "成山": 896, "祭日": 604, "绎": 1326, "从者": 2075, "追念": 415, "家天下": 797, "徐市": 178, "名曰": 2954, "韩终": 30, "侯公": 133, "石生": 404, "深信不疑": 1848, "卜筮": 342, "癸丑": 2601, "出游": 8445, "神药": 1012, "鲛鱼": 141, "无功": 4781, "丙寅": 2974, "高渐离": 1772, "击筑": 265, "中空": 5192, "改名换姓": 299, "酒保": 1036, "隐姓埋名": 866, "担惊受怕": 989, "作客": 6733, "赞不绝口": 2177, "传进": 548, "死罪": 1267, "瞎": 29809, "双眼": 18993, "双目": 3448, "着迷": 6734, "击打": 36802, "殉": 1270, "河南省新乡": 619, "砸在": 3189, "散尽": 1764, "家财": 1052, "误中副车": 17, "微服": 895, "强盗": 6964, "二十天": 1872, "博士官": 92, "私藏": 3878, "西楚霸王": 811, "仙者": 290, "人主": 507, "恶鬼": 1964, "人臣": 699, "入水": 2861, "云气": 1436, "恬": 3742, "四百六十": 149, "若非": 2237, "顶撞": 1894, "怪事": 2107, "荧惑": 377, "心宿": 461, "天蝎座": 10877, "坠落": 11495, "挨家": 195, "查问": 1115, "认罪": 104343, "巡行": 517, "传令": 1109, "夜路": 1419, "拦住": 12191, "捧回": 291, "奇遇": 7044, "好一会": 743, "山鬼": 1842, "轻描淡写": 2161, "应验": 1910, "水神": 1064, "北河": 943, "授给": 289, "有生之年": 3151, "府令": 190, "钱塘": 4522, "边上": 25024, "水波": 1836, "凶险": 1923, "贞节": 643, "刻下": 828, "不贞": 397, "秉义": 159, "咸化": 17, "全篇": 2818, "声势浩大": 1225, "避居": 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"不清": 35239, "片面": 9876, "习练": 848, "长袖": 5578, "创出": 2645, "骋": 2108, "刖": 424, "自暴自弃": 3114, "身残志坚": 402, "胜记": 162, "傥": 1934, "适足": 97, "为善": 1573, "刀锯": 478, "不齿": 1422, "曾嫌": 33, "酬": 10664, "山镇": 25312, "深邃": 5207, "制鞋业": 128, "烧炭": 889, "暗害": 226, "髌骨": 4633, "伤腿": 177, "兽皮": 1152, "过膝": 1439, "皮靴": 1583, "下山": 11523, "毒蛇": 6050, "咬伤": 16073, "樵夫": 1534, "蛇毒": 1258, "攻心": 2017, "砍下": 4420, "腿上": 9668, "救命之恩": 508, "制鞋": 767, "鞋匠": 888, "皮张": 202, "裹缠": 32, "膝部": 1430, "带毛": 376, "去毛": 1255, "后加工": 445, "无烟": 3060, "哟": 59982, "忌恨": 356, "正巧": 2994, "晾": 10251, "露水": 3129, "流进": 1556, "吃完": 47967, "夸奖": 5874, "里加": 5890, "没想到": 69765, "祖师爷": 1383, "流落": 3657, "泥人": 1093, "布阵": 2130, "惠山": 1640, "第八十七": 1065, "回说": 507, "鬼谷": 1373, "流泪": 23496, "劫难": 1905, "第八十八": 735, "佯": 915, "装疯卖傻": 542, "淳于髡": 164, "投井": 300, "布下": 1054, "八门": 1694, "冤情": 414, "啸": 11303, "神怪": 774, "阚": 8034, "郑安平": 74, "海涛": 6977, "孙红雷": 8175, "装疯": 409, "阵中": 2441, "同州": 725, "之斗": 245, "清芬": 538, "太公": 2421, "设问": 1419, "怀疑为": 525, "古墓": 10021, "六卷": 879, "韬略": 560, "武韬": 43, "义经": 1243, "盗得": 126409, "暗合": 639, "功成": 2644, "道藏": 825, "三章": 1947, "传本": 258, "要旨": 733, "功法": 1462, "逾百": 483, "广成子": 252, "蹇": 5428, "言明": 692, "西王母": 1347, "天机": 6165, "陶宏景": 22, "合符": 385, "秘闻": 971, "兵符": 859, "虎口": 3034, "富国": 5348, "安人": 1157, "观天": 506, "七家": 2199, "淑真": 92, "蔡元定": 51, "清夜": 1130, "笈": 1813, "太清": 1916, "玉诀": 34, "法融": 143, "今译": 326, "文津": 610, "遂良": 115, "小楷": 1399, "法玄": 29, "一十九": 218, "文宝": 397, "程子": 464, "老氏": 128, "股掌": 504, "譬": 1036, "陆龟蒙": 231, "献词": 251, "斧凿": 264, "沉厚": 91, "宝字": 270, "数尽": 139, "求战": 479, "战罢": 531, "圣君": 737, "三才": 5440, "万化": 548, "河上": 4511, "高足": 316, "志平": 1443, "岂可": 906, "至理": 585, "窥测": 185, "掇": 3096, "定见": 317, "异事": 705, "异说": 307, "养身": 1405, "制胜": 2884, "术数家": 25, "连镇": 309, "养正": 439, "黄石公": 274, "公武": 316, "被列": 726, "中略": 547, "阿塔": 2700, "葛兰": 1375, "多利纳": 177, "卑南乡": 258, "盗掘": 3282, "绩": 9517, "美术品": 288, "古学": 221, "连用": 5461, "亚历山": 2957, "安科纳": 309, "胡夫金字塔": 467, "数十亿": 1331, "三年代": 10, "特洛伊": 4515, "埃文斯": 2609, "劳埃德": 1423, "斯蒂芬斯": 302, "中心论": 524, "摩尔根": 469, "皮特": 7076, "弗林德斯": 236, "多萝西": 716, "巴斯": 18043, "头骨": 4820, "假使": 2649, "非洲人": 2613, "炼铁": 1201, "公正性": 1151, "地景": 685, "弃置": 1877, "园圃": 384, "声名鹊起": 623, "企图心": 517, "维护法": 134, "纳税人": 45548, "陶瓷学": 20, "古钱": 761, "类型学": 291, "地磁学": 36, "民俗学": 738, "铲": 14392, "张光直": 126, "曹兵": 326, "陈星": 1835, "古器物学": 10, "聊城市": 6827, "滨州市": 3899, "莱芜市": 4364, "泰安市": 6012, "名泉": 533, "山色": 2988, "第二十二届": 312, "黑虎泉": 260, "济阳": 1558, "形成期": 387, "雷泽": 1834, "躬耕": 890, "国改": 88, "济南相": 31, "灵岩寺": 247, "赋诗": 3177, "捣药": 531, "白兔儿": 132, "钢条": 726, "不误": 1928, "万金": 1871, "济南路": 961, "仁杰": 1627, "张养浩": 165, "监工": 1050, "鞭打": 1753, "胶西": 403, "始为": 554, "城内外": 216, "十三万": 548, "赤地": 410, "榛榛莽莽": 5, "手游": 51020, "鬼哭": 683, "洪家": 2170, "大观园": 3401, "特派": 2191, "蔡公时": 91, "虐杀": 3051, "四郊": 349, "马良": 3415, "进城": 12115, "东乡": 3636, "重汽": 991, "长清": 3591, "新泰": 5498, "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": 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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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"温热": 9034, "风速": 13882, "榆树市": 4853, "德惠市": 3496, "汽车厂": 1287, "教育处": 823, "卫生处": 250, "通信处": 86, "热播": 11483, "净月": 1540, "长江路": 5554, "二道河子": 178, "九台市": 2233, "镇原": 3362, "龙家": 1542, "特快": 5889, "爆破": 13952, "大房": 1219, "和郊": 13, "北湖": 2515, "阳线": 2270, "德大": 353, "出车": 1826, "零配件": 3018, "长春客车厂": 120, "重庆路": 1141, "红旗街": 1142, "百货大楼": 2764, "专卖店": 25291, "百脑汇": 1002, "小店": 19395, "名门": 5304, "金安": 2611, "松苑": 269, "名都": 3937, "凯悦": 1779, "十几家": 702, "东北话": 1785, "埋汰": 623, "膈": 3491, "得瑟": 3020, "张扬": 8533, "碜": 670, "难看": 21606, "踝骨": 1863, "叽": 4213, "纸片": 3929, "右转": 209881, "一脚": 18888, "褒义": 3427, "该寺": 3589, "如织": 512, "观音寺": 1463, "林黛玉": 5203, "陈晓旭": 978, "钟磬": 364, "呜": 7958, "绕梁": 910, "绮丽": 1586, "小德": 2230, "毁弃": 395, "年久失修": 1057, "图例": 2060, "从新": 12739, "第一块": 1691, "京都市": 2922, "方格状": 51, "多心": 1656, "加宽": 1816, "旧貌": 287, "精心": 23510, "别致": 5165, "几轮": 1765, "修编": 440, "舒展": 4252, "鲁尔区": 448, "杜塞尔多夫": 1392, "咸菜": 5010, "量足": 559, "荤菜": 1942, "涮锅子": 21, "总能": 13406, 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"进取心": 1627, "音乐声": 688, "交验": 3502, "干酪": 2478, "续展": 2132, "注册簿": 14, "所涉": 2182, "冲淡": 3529, "联结到": 43, "贯彻始终": 257, "创作物": 35, "衍生品": 3305, "第四号": 661, "蓝丝带": 596, "不求": 9465, "双胞胎": 15555, "办起": 576, "法新社": 1718, "中央社": 3221, "经社": 293, "中新社": 1740, "传真社": 60, "俄塔社": 62, "俄新社": 249, "共同社": 457, "时事社": 18, "埃菲社": 59, "德新社": 118, "澳联社": 21, "合众国际社": 155, "伊通社": 5, "朝中社": 399, "安莎社": 41, "联社": 12606, "砹": 603, "烃链": 41, "烷链": 12, "有氧化": 377, "卤化氢": 155, "氢卤酸": 88, "弱酸": 1926, "氢溴酸": 442, "碘酸": 865, "高碘酸": 149, "二氧化氯": 1460, "酐": 3164, "偶极子": 639, "用好": 6478, "力保": 991, "敌强我弱": 148, "缓兵之计": 294, "大哗": 143, "煮成": 4888, "肉羹": 443, "赞曰": 471, "猎得": 224, "西巴": 366, "曩": 944, "罗浮宫": 734, "小段": 9557, "珍罕": 67, "有偿": 5114, "墨西哥市": 127, "搜藏品": 18, "有头有脸": 401, "展示柜": 1136, "贝桑松": 539, "斯隆": 1716, "丽城": 579, "达文西": 873, "蒙娜丽莎": 2818, "两个小时": 14115, "知性": 5053, "贡献给": 860, "艾尔米塔什": 42, "冬宫": 534, "入场费": 607, "每年一次": 1115, "国立博物馆": 879, "儿童博物馆": 146, "集邮": 2214, "地方史": 152, "航空史": 103, "插画": 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"佩英": 201, "希拉蕊": 415, "萧言中": 33, "江欣燕": 212, "郑文灿": 255, "耀明": 750, "坚尼": 1133, "张昊": 377, "伦尼": 527, "镤": 235, "里奥斯": 803, "爱德华六世": 122, "拉萨尔": 378, "贝斯": 6748, "居伊": 410, "殉道者": 749, "奥迪": 39571, "忠一": 391, "梅克伦堡": 2263, "小号手": 414, "亲卫队": 2633, "利铭": 92, "罗德利": 79, "瓦迪": 1824, "克莱斯蒂尔": 37, "黄光": 1612, "麦克纳马拉": 238, "雷击": 3423, "雷声": 2725, "隆隆": 1280, "声响": 8434, "和光": 2802, "声速": 1828, "一公里": 4737, "劈断": 75, "王睿": 426, "炙": 4622, "鱼尾": 2163, "避雷": 881, "四角": 5090, "打在": 9084, "天谴": 1035, "天公": 2840, "犯罪嫌疑人": 43876, "君武": 102, "劈开": 1575, "积雨云": 466, "涡旋": 1176, "亚速尔": 762, "氛气": 18, "雪雾": 75, "和云": 2009, "云滴": 356, "蒸腾": 1796, "水滴": 9235, "冰晶": 3274, "低湿度": 85, "绝缘体": 1716, "导通": 2833, "美国航天局": 559, "光谱分析": 549, "瓶身": 1730, "水珠": 3985, "瓶子": 24782, "烧水": 4040, "煮饭": 6651, "附著": 1327, "锅子": 2753, "吸湿": 2648, "飘浮": 1917, "机师": 1955, "三时": 3573, "宰赫兰": 51, "误差值": 376, "米沙鄢": 244, "民答那峨岛": 120, "格利": 3641, "麦哲伦": 2299, "美菲": 392, "小虎": 11337, "数十万年": 116, "米南加保": 107, "侨领": 227, "统揽": 90, "谢里夫": 901, "托市": 497, "宣达": 108, "砍死": 4219, "菲力普": 287, "殖民军": 153, "杜菲": 191, "戒严法": 118, "马可仕": 250, "首季": 1104, "科拉松": 84, "艾斯特拉达": 40, "诺诺": 1472, "达沃市": 167, "特以": 460, "热季": 243, "碧瑶": 1430, "冷锋": 1404, "掠过": 4108, "佬沃": 34, "夏秋季": 753, "宿务": 356, "墨宝": 724, "马拉坎南宫": 48, "志愿兵": 888, "巡逻艇": 860, "班牙": 52, "伊巴丹": 79, "西乐": 406, "雷人": 2701, "十之八九": 770, "中华人": 400, "同化成": 92, "已否": 44, "百分之八": 518, "菲华": 62, "惠安县": 6373, "南安县": 297, "海澄": 1023, "龙海市": 6359, "投资人": 25215, "圣玛丽亚": 447, "亚高": 215, "菲国": 415, "高人": 19653, "阿丽": 1759, "拉瑙": 127, "达兰": 478, "禁毁": 192, "毒瘤": 3070, "披索": 258, "产经": 355, "铬铁矿": 158, "巴拉望": 595, "冠绝": 760, "三分一": 420, "光景": 2896, "打拼": 8143, "买盘": 1580, "铺有": 615, "吉普车": 5849, "民航局": 1468, "长才": 561, "涵盖面": 421, "漂染": 459, "港市": 535, "哥伦": 656, "百胜": 1601, "东南端": 958, "盛地": 124, "陶杰": 384, "嫁到": 3772, "后处理": 2061, "民答那峨": 35, "沙耶夫": 114, "巴丹岛": 105, "内格罗": 525, "吉德": 1411, "掳获": 834, "贾村": 537, "布哈": 284, "穆斯": 2481, "继父": 11097, "逃家": 264, "终其一生": 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518, "国金": 1276, "赛门铁克": 442, "普惠": 4648, "永亨": 302, "友利": 369, "华一": 505, "年成交额": 32, "上亿元": 525, "骡马": 1017, "来福": 2093, "巷子": 18750, "琴台": 1241, "风情街": 755, "磨子": 764, "均达": 726, "大户": 6713, "通览": 316, "现成": 2124, "张王": 514, "罗姓": 222, "全市性": 80, "惠泽": 683, "槎": 5129, "春灌": 59, "西华门": 525, "号牌": 156167, "和谐号": 1007, "紧锣密鼓": 905, "内环路": 1710, "中轴路": 46, "蜀都大道": 326, "红星路": 2167, "北新": 2465, "成绵": 1193, "成南": 408, "隆昌": 3966, "客站": 2249, "五桂桥": 263, "茶店": 2472, "块石": 918, "十陵": 896, "成都站": 929, "岛式": 7085, "机票": 35095, "祥鹏": 202, "太平寺": 324, "葭": 2212, "眼界": 10919, "麻将馆": 8341, "劼人": 110, "艾芜": 97, "沙汀": 288, "串串": 7607, "老妈": 29763, "担担面": 672, "水饺": 5450, "盘飧": 75, "品三": 153, "叶儿": 540, "下河": 1446, "回锅肉": 2238, "汤底": 1853, "汤锅": 2748, "上树": 1433, "蒜泥": 3858, "怪味": 1930, "鸡粉": 3265, "坛子": 3519, "干煸": 1467, "鳝": 2788, "鸡冠山": 366, "安仁": 3399, "憨态可掬": 477, "翠竹": 2386, "产房": 2503, "兽医院": 302, "放养区": 20, "红火": 3770, "桃花节": 270, "赛歌会": 15, "喜神": 1677, "心目": 741, "岁修": 92, "春耕": 993, "五谷丰登": 885, "渠首": 252, "鱼嘴": 1057, "工程处": 2869, "片外": 190, "调侃": 16610, "地区电视台": 115, "李伯": 1250, "男神": 20450, "你妈的": 6876, "T恤": 17120, "市民化": 274, "省报": 90, "七九": 1184, "七中": 1439, "树德": 1087, "校部": 161, "东软": 800, "球市": 236, "四川全兴": 104, "全兴队": 23, "场场爆满": 320, "黎兵": 172, "冠城": 565, "四川队": 379, "投资方": 4839, "哈达迪": 405, "球星": 15109, "银杏叶": 1391, "和平鸽": 462, "挥手": 4451, "第三十条": 1519, "第九十五": 472, "市管": 558, "重编": 639, "特级": 5243, "镇村": 1718, "锡利": 154, "市政局": 2172, "老来": 2766, "从子": 890, "家庭主夫": 133, "心机": 11062, "背地里": 1630, "小动作": 4567, "父权制": 279, "背靠背": 1678, "谭良德": 26, "李宏平": 13, "李孔政": 12, "熊倪": 146, "裸照": 4660, "撞伤": 12226, "孟德": 1302, "曹腾": 208, "费亭侯": 22, "曹嵩": 225, "机变": 1080, "桥玄": 146, "何颙": 98, "奸贼": 165, "听罢": 606, "后手": 1926, "越墙": 259, "县警察局": 62, "违禁": 2196, "堂妹夫": 27, "议郎": 555, "陈蕃": 499, "严令禁止": 205, "潜逃到": 164, "王芬": 113, "打草惊蛇": 478, "逼走": 830, "惹祸": 1025, "上身": 12288, "抓去": 1334, "乡野": 896, "乡勇": 377, "揭竿": 213, "勃海": 339, "王匡": 376, "英雄豪杰": 717, "马首是瞻": 351, "汴水": 328, "坐骑": 14053, "这么少": 2191, "一整天": 9627, "欢宴": 358, "谷关": 478, "险要": 1455, "不败之地": 1265, "建平": 3333, "粮秣": 146, "武阳": 1459, "本营": 177, "治中": 622, "寿张": 461, "逼退": 795, "其木": 239, "三十余万": 120, "大名鼎鼎": 3559, "发干": 2259, "屯军": 171, "鄄城": 588, "封丘": 462, "鸡犬": 1189, "雍丘": 245, "张超": 1852, "鏖战": 1450, "总算": 7534, "丙戌": 3133, "高顺": 465, "穷途末路": 810, "衣带": 736, "车胄": 93, "会战史": 7, "淳于": 1153, "败逃": 420, "蹋": 1293, "寒族": 82, "深通": 182, "重才": 137, "枭雄": 6123, "请降": 434, "惊疑": 225, "虎步": 238, "濡须口": 122, "安徽巢湖": 187, "军容": 663, "丙申": 2915, "广及": 145, "刘姓": 1271, "改授": 315, "远游": 1308, "紧闭": 3225, "披发": 452, "牵出": 823, "鸩": 915, "站住脚": 461, "班师回朝": 221, "旌旗": 2104, "旒": 1023, "副车": 148, "征西将军": 457, "罢兵": 361, "详请": 106, "姻亲": 1302, "阳平关": 477, "硝烟": 2993, "分进合击": 77, "无功而返": 930, "鸡肋": 7484, "向关羽": 39, "射到": 1947, "陈说": 403, "陈群": 653, "除灭": 229, "廿一日": 267, "廿九日": 278, "薤": 1087, "短歌行": 462, "夏门": 280, "自明本志令": 47, "雄心壮志": 1512, "奋发进取": 169, "杂有": 266, "朝露": 985, "游仙": 3164, "南征北讨": 83, "惨象": 153, "雄图": 344, "哺": 2232, "志在千里": 398, "星落": 339, "殒": 751, "去日": 699, "低沈": 59, "蹊": 1918, "词藻": 944, "激昂慷慨": 86, "直抒": 222, "襟怀": 697, "竦": 1137, "丰茂": 623, "星汉": 864, "算好": 6552, "魏氏": 876, "陋": 2470, "床头": 16363, "雅望": 429, "直当": 36, "浸润": 6126, "摈": 430, "岂不": 6141, "杖责": 173, "不疑": 1657, "曹营": 1136, "两面性": 1675, "家务事": 1577, "奸伪": 39, "奸猾": 177, "应付裕如": 54, "乱兵": 251, "坑杀": 698, "有孕": 885, "多自": 203, "伯母": 1383, "心中不安": 87, "有夫之妇": 525, "城阳": 2338, "失德": 246, "鲈鱼": 3498, "求新求变": 267, "夏侯楙": 115, "林国": 760, "清还": 377, "奸诈": 1488, "敬则": 350, "丑事": 680, "人所共知": 450, "连缀": 318, "王素": 359, "安徽亳县": 75, "抱养": 1094, "宁生": 113, "所患": 774, "悦服": 61, "吝": 1510, "实者": 188, "辅翼": 104, "田丰": 1171, "乘虚": 519, "无及": 236, "久未": 978, "厚恩": 165, "共死": 93, "关张": 849, "老贼": 1051, "由得": 403, "雄杰": 222, "其锋": 186, "其弊": 164, "若不然": 84, "安坐": 434, "明哲": 923, "享福": 1531, "万全之策": 166, "王沈": 223, "残虐": 335, "不世": 873, "苦毒": 84, "德广": 172, "虎踞": 1226, "鞭挞": 849, "宇内": 435, "其器": 241, "矫情": 12722, "旧恶": 124, "武帝纪": 572, "痛惜": 771, "张茂": 473, "曹孟德": 369, "默然": 1167, "难易": 4197, "卒胡": 3, "建威": 514, "协建": 23, "灵符": 1073, "逾时": 179, "相抗": 266, "焉用": 333, "荀文若": 55, "槊": 1208, "张辅": 326, "贾文": 420, "祖之": 345, "无已": 372, "灵变": 54, "流年": 9174, "漳河水": 42, "松风": 2040, "愁人": 1159, "眦": 2527, "华屋": 662, "桑榆": 686, "汗漫": 472, "娥眉": 618, "丧败": 25, "走强": 1836, "束手": 760, "任情": 120, "临事": 305, "近古": 363, "独行": 3773, "慕德": 239, "犹且": 86, "蕤": 827, "康西": 580, "磊磊": 1003, "皎然": 344, "载记": 550, "逆乱": 144, "俯仰": 2694, "故业": 79, "百世": 3158, "骄矜": 229, "临危": 1704, "万乘": 1010, "同乎": 66, "沈溺": 50, "临难": 171, "谈笑": 3387, "咿": 1309, "留连": 1809, "妾妇": 101, "生畏": 551, "胙": 486, "蝼蚁": 1148, "回天": 1179, "知人善任": 384, "眩": 3743, "微贱": 193, "安闲": 638, "峻急": 28, "芟": 505, "平海": 913, "逡巡": 780, "不胜": 12584, "智勇": 1824, "惮": 1413, "生风": 1361, "如云": 3238, "苦寒": 1873, "不惟": 336, "秦川": 1542, "长笑": 218, "垂头": 526, "劳民": 132, "八荒": 1776, "勍": 367, "遗恨": 1284, "陈迹": 1540, "雉堞": 229, "蝈": 199, "文心": 1171, "眼底": 4988, 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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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"麦加朝圣": 159, "图西": 357, "行山": 560, "可圈可点": 2499, "技术史": 113, "杂揉": 77, "在一块": 5390, "具像化": 50, "奥德修斯": 783, "阿奇里斯": 55, "柯灵乌": 21, "称得": 225, "蓬勃向上": 263, "荡气回肠": 1311, "异邦": 485, "丰功伟业": 195, "奇闻轶事": 145, "高出一筹": 159, "此风": 312, "俘掳": 113, "泱泱大国": 452, "在理性": 494, "六零年代": 123, "磁共振": 3371, "岩壁": 1282, "工具化": 234, "莫雷利": 105, "医疗史": 45, "医药史": 31, "本集": 2696, "妙闻": 35, "会阴": 2891, "切除术": 6755, "耳鼻喉": 3979, "返老还童": 1091, "催情": 753, "阗": 474, "药丸": 17953, "交叉处": 1164, "正典": 804, "提纲挈领": 499, "修订成": 29, "历史说": 102, "闪闪发亮": 930, "化脓": 3880, "发绀": 553, "维持为": 80, "缝针": 1819, "法律学": 404, "脑室": 1967, "性反常": 19, "家蚕": 443, "热时": 3895, "丁酸": 1423, "消毒药": 369, "注射到": 665, "结核杆菌": 926, "瘤状": 404, "报酬率": 918, "李森": 294, "工资率": 131, "两重性": 247, "奥沙": 582, "代扣": 3596, "受益人": 4548, "自利": 847, "手工艺人": 215, "查维斯": 237, "连合会": 375, "职业介绍所": 289, "外包给": 1129, "二合一": 2218, "硕大": 2643, "拉西拉": 6450, "搜寻者": 133, "冥府": 879, "反射率": 1159, "反射镜": 1109, "激光束": 1072, "钨丝灯": 218, "跃上": 541, "激发为": 16, "长春光机所": 53, "光量子": 468, "莱塞": 603, "新生事物": 999, "钕": 959, "低功率": 741, "解理面": 66, "灭蚊": 1386, "条码": 5093, "扫描仪": 4738, "非接触性": 85, "刮胡刀": 1200, "刀片": 10824, "眼角膜": 1432, "护目镜": 1171, "断路器": 5561, "李龙": 1295, "酒廊": 678, "拍剧": 1007, "八三年": 112, "日本车": 2453, "基爱": 147, "神雕侠侣": 10956, "房客": 4992, "无名火": 300, "锦辉": 558, "老友鬼鬼": 66, "梁中书": 210, "独臂": 1300, "刘志明": 50, "秃笔": 147, "翠花": 1802, "宋世杰": 321, "王虎": 649, "特警": 13462, "老千": 1698, "骑呢": 222, "大状": 919, "守义": 339, "胜者为王": 973, "陆文": 297, "豹子胆": 153, "新市": 2807, "岳家": 972, "大泉": 4258, "泞": 287, "烂泥": 1561, "兰溪镇": 256, "镇岳": 683, "两千二百": 41, "部令": 175, "搞糟": 126, "叠叠": 503, "使全": 325, "案名": 101, "研订": 64, "后里": 1295, "丰丰": 333, "征征": 49, "干干": 2331, "冲冲": 4074, "沈淀": 140, "苹苹": 124, "松松": 1420, "困困": 260, "仆仆": 186, "晒晒": 3821, "伙伙": 116, "朱朱": 1352, "卜卜": 203, "镟": 180, "克克": 375, "咸咸": 145, "吁吁": 52, "准准": 109, "合合": 451, "洒洒": 640, "夸夸": 1305, "芸芸": 818, "吓吓": 529, "叶叶": 963, "价价": 45, "当当": 6878, "车车": 3549, "兴兴头头": 8, "实实": 299, "劝劝": 1143, "鸡鸡": 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, 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512, "对公": 5047, "商书": 59, "常识性": 1329, "宗合": 55, "砥石": 197, "卒子": 507, "立昌": 250, "屯南": 408, "甲上": 557, "一千一百": 620, "典册": 82, "鸟禽": 133, "董温": 8, "三统历": 34, "大庚": 37, "尚健": 126, "徂": 1005, "甲有": 3912, "恶兆": 144, "七十五年": 135, "济宁市": 10438, "曲阜市": 2199, "兴师动众": 273, "状貌": 229, "皮袋": 351, "盛满": 1221, "射破": 144, "辰国": 85, "大商": 1490, "凑成": 1032, "商邱": 54, "妇好": 402, "旧藏": 328, "征人": 660, "方殷": 68, "叶王": 178, "戈矛": 306, "斧钺": 280, "刀斧": 351, "征婚": 3239, "陕西户县": 68, "姬昌": 532, "丰邑": 181, "白陶": 108, "平纹": 498, "绉纱": 88, "长途贩运": 41, "桑林": 390, "大护": 165, "一千零七十": 28, "常压": 1073, "觥": 1065, "三百六十五": 721, "微凸": 256, "面镜": 823, "照出": 936, "地神": 347, "问卜": 186, "帝子": 622, "诸项": 126, "非我族类": 385, "牧誓": 44, "世则": 97, "卑下": 306, "乃父": 197, "下辈": 276, "成规": 325, "庸国": 123, "下家": 3295, "铁云藏龟": 39, "菁菁": 1224, "寿堂": 399, "林林": 1407, "写写": 6822, "容庚": 76, "续续": 285, "佚存": 14, "六六": 2887, "郭若愚": 8, "远古史": 41, "小雨": 14643, "汪涛": 290, "尖锥": 143, "盘大": 667, "克鼎": 72, "下口": 1950, "王昌": 888, "开枝散叶": 238, "赎回来": 285, "进献给": 112, "礼贤下士": 282, "虢仲": 87, "虢叔": 103, "沣水": 112, "骤逝": 182, "屡战屡胜": 169, "管城": 4155, "伯达": 344, "难以下咽": 677, "山东滕县": 69, "江苏泗洪": 83, "掌控力": 532, "大家长": 399, "虎方": 133, "新蔡": 684, "造父": 775, "宗氏": 212, "合攻": 195, "吉甫": 410, "掌权者": 681, "伯阳父": 26, "陕西华县": 102, "迁西": 407, "土伯": 100, "外平": 45, "子禽": 54, "河南原阳": 60, "贤名": 140, "石门山": 266, "黩武": 116, "湖北随县": 80, "大伤": 546, "周平": 699, "国野": 106, "野鄙": 7, "整齐划一": 941, "侯甸": 12, "太祝": 176, "事务官": 485, "职级": 2027, "宗子": 277, "小宗": 365, "绝户": 250, "子子孙孙": 562, "卿卿": 708, "祀神": 603, "德德": 531, "敬德": 535, "乱杀": 264, "礼嘉": 945, "见礼": 97, "宴饮": 494, "周公之": 152, "罍": 984, "翼城": 452, "燕君": 113, "时迁": 702, "重人": 177, "槐河": 22, "位尊": 234, "山东定陶": 54, "齐与": 154, "陕甘晋": 29, "商武": 40, "湖北宜城": 60, "近卫军": 1026, "千夫": 417, "尊容": 219, "庶殷": 2, "粗笨": 132, "侍侯": 95, "侍役": 14, "寺人": 116, "隶农": 42, "竹鼠": 812, "起土": 71, "抛荒": 152, "禾谷": 219, "易居": 1375, "翻制": 89, "食器": 305, "酒器": 880, "规格化": 224, "瓷片": 1316, "矽石": 49, "斜纹": 1320, "刺绣品": 89, "骨铲": 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"头份": 936, "雾峰": 1628, "员林镇": 169, "土库镇": 233, "麦寮乡": 250, "林森二路": 8, "甲仙": 464, "恒春镇": 238, "林森路": 201, "苏澳镇": 352, "罗东镇": 670, "宜兰市": 546, "光复乡": 186, "苑里镇": 256, "淮海路": 1415, "南极点": 822, "地极": 252, "难抵": 220, "极圈": 239, "几百米": 1710, "斜射": 458, "身置": 41, "站区": 2347, "捕猎船": 10, "随行人员": 555, "费恩斯": 62, "送风": 1848, "蒸气机": 243, "压气": 1153, "手键盘": 89, "低声细语": 86, "引以为豪": 1020, "风道": 1608, "管长": 532, "栋材": 116, "豫鄂": 362, "令居": 85, "就中": 2103, "版刻": 55, "羖": 110, "陶岔": 86, "丁已": 190, "铁矛": 96, "大败于": 246, "宋留": 44, "大农": 365, "张曼成": 62, "战宛城": 55, "岗上": 596, "三月底": 559, "南船": 200, "邓县": 209, "陆营": 105, "全村": 3169, "烧光": 607, "戳死": 299, "王村乡": 165, "赵庄": 971, "桐柏": 1722, "南召县": 2984, "桐柏县": 3649, "方城县": 4707, "西峡县": 3547, "承审": 129, "电视电话": 676, "试点县": 284, "河南油田": 189, "非农": 2303, "灌河": 429, "湍": 2727, "压水": 1103, "核电机组": 325, "油气流": 33, "一亿七千万": 57, "高铝": 263, "种苗": 1661, "毛躁": 1510, "达意": 877, "川内": 1080, "郭庄": 1106, "李二": 2332, "封丘县": 2612, "靳岗": 106, "安巴都": 11, "垦植": 58, "产销量": 406, "普康": 204, "防爆": 6392, "桔梗": 4652, "丹参": 3877, "麦冬": 3452, "栀子": 2546, "板蓝根": 2836, "福森": 192, "勘探局": 470, "中烟": 686, "华光": 1818, "减振器": 882, "牧原": 337, "邮政储蓄": 24860, "宏源": 1359, "程控": 685, "焦枝": 80, "区域型": 138, "中国交通部": 55, "月山": 1072, "航空站": 715, "自行车道": 1842, "摩的": 3076, "扛鼎": 547, "骈俪": 75, "二月河": 396, "作家群": 174, "徐惟诚": 16, "坠子": 1446, "王店": 1674, "有社": 168, "圆滑": 4616, "撵": 3351, "屋外": 5444, "风吹日晒": 1004, "拿回去": 1685, "琳琅满目": 2115, "紫晶": 1085, "石佛寺": 763, "烙画": 104, "役用": 74, "大碗茶": 651, "盖碗茶": 183, "茶具": 3851, "配置到": 458, "糊辣汤": 112, "豆腐脑": 2567, "烩面": 1752, "苞谷": 584, "糁": 1038, "土岗": 199, "十四所": 83, "中国林科院": 58, "志学": 293, "关镇": 2444, "雕绘": 53, "关古": 75, "哄动": 156, "造山带": 487, "蝙蝠洞": 197, "鹳": 1715, "水帘": 495, "太白顶": 37, "五笔字型": 332, "王永民": 70, "南阳籍": 6, "许永跃": 42, "秦英": 154, "董栋": 76, "史瓦西": 638, "广获": 188, "递升": 114, "系里": 2053, "类时": 410, "具体说来": 725, "基性": 595, "守恒律": 131, "散度": 574, "对应物": 139, "并非易事": 737, "蓝移": 225, "夏皮罗": 238, "点会": 1474, "能层": 70, "波源": 502, "啁啾": 436, "一百四十亿": 16, "定态": 236, "推知": 632, "尝试性": 490, "有前途": 8794, "剖分": 382, "既然如此": 2481, "克氏": 1527, "接生婆": 246, "埋葬掉": 32, "急待": 426, "造影": 8078, "运行地": 34, "残迹": 711, "现代语": 139, "恣意": 1263, "功成名就": 2029, "相形之下": 336, "劳苦大众": 602, "趋附": 77, "采认": 88, "可见得": 217, "越越": 1238, "越汉": 19, "干城": 703, "苗族自治区": 50, "凤凰花": 465, "松桃苗族": 1911, "来凤县": 1627, "交颈": 139, "海拨": 338, "吾乡": 848, "纱布": 11543, "柯达伊": 67, "芙洛": 986, "震撼力": 821, "三十二个": 222, "瓦坎": 646, "萨波": 497, "杜兰戈": 159, "涉毒": 2573, "反毒": 427, "仙人掌": 8325, "墨国": 58, "内向型": 574, "负债率": 840, "塞迪略": 48, "卡州": 510, "九千万": 217, "键钮": 83, "民族舞": 970, "风笛": 982, "推拉": 6222, "节省下": 206, "马家窑": 244, "牛骂头": 130, "店子": 3910, "亮岛": 53, "莒光": 519, "地土": 152, "茶乡": 348, "松土": 1100, "衡东": 344, "陵县": 1531, "租米": 26, "租钱": 23, "万寿山": 491, "分巴": 32, "官渡镇": 825, "连州": 1952, "骄兵": 103, "直赴": 71, "长吏": 275, "南郭": 670, "兴教": 207, "十室九空": 138, "疲沓": 61, "声震中外": 2, "刘镐": 20, "大真": 452, "蓉园": 84, "市两": 177, "康平县": 1341, "东陵区": 1892, "辽中县": 2850, "太阳鸟": 347, 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"好好好": 1983, "农神节": 71, "拉托娜": 16, "王俊生": 102, "南勇": 294, "杨一民": 243, "崔大林": 93, "三优": 112, "源本": 94, "呆头呆脑": 1302, "弃暗投明": 307, "弦儿": 130, "阿雪": 313, "税租": 25, "改法": 647, "萨利纳斯": 155, "安菲特里忒": 44, "内贸": 441, "量器": 226, "方尺": 72, "亚平宁": 206, "斯托内": 36, "旧框框": 28, "预料之中": 529, "着数": 637, "导洞": 82, "老鼠洞": 202, "银团": 405, "电车线": 541, "竖井": 1164, "弃土": 307, "透水性": 543, "管片": 455, "滑坡体": 203, "岩块": 318, "通风管": 382, "赛福林": 3, "信号系统": 958, "限界": 978, "挂接": 408, "替代性": 1677, "一列列": 243, "轴距": 6030, "两段式": 315, "侧向力": 207, "灭火剂": 1000, "侧线": 1946, "习尚": 108, "露齿": 762, "俵": 901, "一时之困": 9, "数百万吨": 69, "氘化锂": 70, "埃内韦塔克": 66, "暖瓶": 568, "岩燕": 42, "河燕": 44, "崖沙燕": 15, "北红": 120, "翎毛": 193, "原鸽": 49, "酒樽": 309, "巨鸟": 236, "跑步机": 5953, "希吉": 148, "叶斯里卜": 14, "咕咕声": 72, "珠颈斑鸠": 279, "绿斑鸠": 2, "上背": 369, "两胁": 252, "钩曲": 90, "鹦鹉学舌": 246, "大紫": 225, "花头": 852, "灰头鹦鹉": 25, "鸡尾": 394, "宠物店": 4819, "瓦氏": 336, "摇摇摆摆": 391, "角鸮": 273, "面盘": 143, "耳羽": 135, "上眼睑": 1068, "领角鸮": 86, "唾余": 35, "弃巢": 31, "较大者": 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"共容": 57, "南京市人": 95, "申戒": 17, "正著": 119, "资本化": 1473, "实需": 20, "麦格赛赛": 4, "陈木在": 8, "五十一篇": 7, "二十篇": 225, "确属": 679, "近史所": 46, "渔歌子": 278, "遐方": 87, "相见欢": 429, "生查子": 255, "漏子": 261, "阮郎": 193, "画堂": 836, "钗头凤": 639, "满庭": 1285, "莺啼": 825, "仙姿": 248, "仄仄": 2822, "巷陌": 434, "不消": 1449, "却道": 872, "鸥鹭": 936, "搅碎": 655, "实像": 548, "织构": 243, "哈洛斯": 12, "玉武": 204, "名归": 75, "薰风": 935, "快马加鞭": 508, "倒海翻江": 50, "深院": 702, "白苹": 748, "倚望": 105, "闲到": 168, "恨恨": 374, "空落": 179, "过尽": 701, "斜晖": 531, "正芳": 221, "轻尘": 566, "孤舟": 2275, "偷香": 275, "贪睡": 835, "攀折": 408, "帘头": 131, "自照": 380, "子婿": 77, "搔首": 677, "豆叶": 146, "画屏": 1194, "浩渺": 618, "恨人": 224, "赵令畤": 39, "雨帘": 152, "起坐": 548, "离愁": 1493, "温婉": 3633, "喟叹": 201, "汪元量": 56, "渐转": 104, "醇雅": 28, "枯寂": 116, "失之偏颇": 113, "词匠": 6, "叶公绰": 6, "磁盘缓存": 201, "柳梢": 724, "绿围": 20, "何许": 1264, "纹丝": 125, "披襟": 341, "柔肠": 392, "春春": 2943, "催花": 366, "栏干": 655, "衰草": 604, "春事": 999, "劝君": 1902, "燕巢": 600, "容光": 244, "重楼": 2544, "眉峰": 611, "住住": 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"鬓上": 93, "把盏": 219, "凄然": 776, "蛙声": 676, "两三点": 1489, "茅店": 437, "忽见": 1621, "如狂": 266, "纨袴": 55, "下半生": 859, "冤仇": 345, "舟人": 412, "消金": 99, "阑珊": 1102, "罗衾": 112, "贪欢": 315, "沉埋": 110, "壮气": 239, "蒿莱": 289, "新绿": 1059, "遮断": 452, "浑如": 454, "征鞍": 284, "芭蕉林": 61, "绿阴": 1096, "斜横": 92, "波痕": 147, "偷得": 2474, "凝思": 218, "眉心": 1014, "渐觉": 478, "夜深": 4795, "归期": 1607, "未语": 163, "书艺": 164, "淘尽": 632, "惯看": 324, "夜阑": 1266, "乱纷纷": 119, "寒秋": 405, "物我": 557, "剩有": 267, "袖里": 322, "罗裳": 311, "寒雪": 415, "喧阗": 179, "丛里": 279, "蛱": 363, "凤栖梧": 140, "如练": 350, "青杏": 306, "鬓影": 155, "徊徨": 29, "伤离": 244, "草色": 1530, "对酒当歌": 626, "垂垂": 517, "阅尽": 567, "留不住": 3706, "尼古拉斯克": 10, "云状": 223, "气壳": 48, "小冲": 677, "客舟": 344, "翠黛": 150, "春红": 689, "暗恨": 154, "林玉山": 86, "知了": 2500, "螓": 86, "蚱蝉": 41, "蟪蛄": 154, "须状": 97, "若虫": 845, "前脚": 1691, "尾节": 127, "发音器": 68, "产卵管": 77, "鼓室": 687, "气室": 640, "破土而出": 424, "蛹状": 7, "寄生蜂": 294, "寄生蝇": 68, "二春": 207, "凤彬": 67, "灰常": 4422, "阿拉法": 87, "末后": 583, "耳闻目睹": 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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, 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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": 635, "放倒": 3268, "狗年": 2045, "麦寮": 527, "仑背": 352, "刘正忠": 4, "嘉铭": 360, "伊格言": 51, "大士爷": 139, "跷跷板": 1071, "奋起湖": 173, "阿里山区": 35, "阿里山茶": 20, "麻薯": 340, "小米酒": 92, "平地人": 22, "医疗网": 69, "九十一年": 101, "祝山": 178, "神木线": 16, "休憩区": 170, "外伞顶洲": 64, "八公里": 455, "美村": 2719, "达邦": 220, "瑞峰": 467, "来吉": 85, "触口": 139, "牛挑湾": 41, "八卦山脉": 51, "彰云嘉": 8, "丁绍仪": 6, "东瀛识略": 5, "意取": 82, "洽公": 76, "画定": 39, "彰化区": 35, "员林区": 34, "溪湖镇": 226, "二林镇": 241, "福兴乡": 265, "花坛乡": 207, "社头乡": 206, "永靖乡": 183, "大村乡": 235, "伸港乡": 131, "埔心乡": 233, "芳苑乡": 195, "埔盐乡": 183, "埤头乡": 179, "田尾乡": 140, "二水乡": 170, "竹塘乡": 159, "彰化县长": 122, "芬园": 343, "山头林立": 60, "陈素月": 26, "兴贤书院": 33, "展示室": 158, "南北管": 22, "区渔会": 127, "彰滨": 166, "艺馆": 240, "源头活水": 478, "采辑": 30, "村史": 370, "亚南": 785, "陈景容": 16, "维瑜": 2, "荣礼": 30, "吴晟": 114, "原洪": 37, "吴庆堂": 21, "陈恒嘉": 10, "王宗仁": 48, "林佳慧": 41, "员生": 144, "埔盐": 287, "埤头": 584, "伸港": 95, "猫罗溪": 83, "永乐街": 202, "田尾": 545, "八卦窑": 19, "大肚溪口": 20, "炸粿": 13, "果苗": 257, "肉饭": 139, "黄石城": 103, "赵守博": 86, "黄宗乐": 15, "周清玉": 48, "翁金珠": 94, "卓伯源": 123, "黄明和": 21, "游月霞": 19, "周柏雅": 34, "教育局长": 382, "颜建发": 11, "建富": 123, "黄昭顺": 131, "林重谟": 38, "陈进丁": 14, "林淑芬": 180, "尤美女": 129, "应晓薇": 121, "蔡明宪": 47, "叶荣钟": 60, "谢荣": 188, "许常惠": 76, "林俊颖": 6, "陈思宏": 8, "杨庆煌": 94, "陈明真": 391, "圳寮": 107, "粘立人": 38, "道卡斯": 62, "播散": 928, "中港溪": 191, "大安乡": 203, "芎林乡": 174, "北埔乡": 176, "上坪溪": 59, "油罗溪": 54, "上坪": 508, "秀峦": 47, "新竹县长": 110, "邱镜淳": 144, "海梨柑": 12, "刘还月": 26, "徐仁修": 19, "大山背": 57, "仰德": 122, "六福村": 128, "廪生": 319, "北郭园": 54, "重拟": 42, "狮头山": 257, "南庄乡": 257, "三湾乡": 173, "向天湖": 47, "枫红": 479, "葛乐礼": 72, "二重里": 18, "前县长": 90, "范振宗": 58, "和祥": 413, "大霸": 105, "秀姑峦山": 51, "岸区": 35, "石梯坪": 35, "岩盘": 105, "海蚀沟": 18, "洄澜": 100, "玉里镇": 214, "吉安乡": 208, "万荣": 968, "卓溪乡": 144, "美仑": 357, "寿丰": 209, "丰滨": 120, "烟楼": 147, "池南": 168, "林聪惠": 2, "林田山": 100, "标柱": 47, "阿美族人": 170, "王惠珍": 31, "王浩威": 15, "李潼": 31, "马得": 185, "陈义芝": 33, "童童": 1057, "国扬": 91, "苍郁": 137, "盐寮": 108, "员山乡": 180, "壮围乡": 188, "五结乡": 293, "冬山乡": 254, "三星乡": 214, "南澳乡": 211, "擅闯": 321, "乌石港": 124, "猎场": 13598, "冬山河": 167, "畚箕": 190, "五结": 377, "壮围": 222, "裂解厂": 58, "六轻": 382, "宜兰人": 92, "龙目井": 102, "外澳": 110, "北二高": 248, "北宜高": 149, "冬山": 332, "南天宫": 108, "开成寺": 23, "永安宫": 229, "茂兴": 162, "翠峰湖": 56, "双连埤": 43, "哈盆": 29, "莺石尖": 2, "澳花": 50, "松罗湖": 6, "生态馆": 63, "碗盘": 328, "菲菲": 10559, "聪明伶俐": 1233, "鸭赏": 17, "熏烤": 675, "冲茶": 390, "洋菜": 222, "刨出": 229, "碎末": 1569, "春卷皮": 296, "里肌肉": 101, "爆香": 8709, "熬出": 1153, "蒜苗": 3668, "压成": 2487, "盐巴": 1186, "葱油饼": 941, "宜兰县籍": 3, "胡万川": 8, "林焕彰": 24, "黄春明": 229, "大竹围": 84, "廷翰": 75, "郑瑞城": 47, "吴静吉": 33, "游干桂": 6, "赖英照": 31, "黄煌雄": 64, "刘守成": 51, "陈金德": 103, "张川": 337, "张金策": 68, "阮义忠": 67, "许芳": 193, "杨奉琛": 21, "乱弹阿翔": 83, "陈旺欉": 13, "简文秀": 13, "吴乙峰": 36, "蔡建伟": 22, "张士凯": 7, "荖浓溪": 225, "主支流": 24, "河阶": 277, "下淡水溪": 100, "泥岩": 733, "阿公店溪": 34, "典宝溪": 23, "隘寮溪": 85, "阿公店": 105, "燕巢乡": 19, "冈山镇": 65, "阿莲乡": 28, "路竹乡": 42, "内门乡": 27, "长治乡": 141, "麟洛": 261, "梓官": 213, "桥头乡": 226, "凤山郡": 129, "大树乡": 85, "鸟松乡": 31, "旗山区": 237, "旗山镇": 45, "钟铁民": 36, "澄清湖": 419, "竹仔门": 32, "凤鼻头": 91, "黑派": 49, "余陈月瑛": 95, "余玲雅": 18, "余政道": 58, "林仙保": 50, "红派": 86, "林岱桦": 84, "钟绍和": 24, "征旅": 12, "许铭杰": 36, "王家祥": 28, "刘保佑": 45, "崎顶": 239, "狮潭": 449, "鹿场大山": 27, "火炎山": 111, "头屋乡": 134, "空旷处": 136, "原乡人": 191, "泰雅族人": 141, "造桥乡": 138, "西湖乡": 246, "公馆乡": 173, "铜锣乡": 230, "社教站": 6, "全国赛": 273, "三十几": 2047, "苗栗区": 28, "风光一时": 165, "台肥": 101, "探采": 84, "刘政鸿": 166, "徐耀昌": 82, "水梨": 255, "樟脑油": 99, "一叶兰": 348, "桂竹笋": 17, "佛手瓜": 519, "金花石蒜": 14, "文旦柚": 50, "桂花巷": 145, "草莓园": 227, "炮城": 31, "赛夏": 143, "陶窑": 106, "古董车": 178, "劝化堂": 27, "鲤鱼潭": 276, "福德祠": 422, "王幼华": 8, "谢霜天": 11, "罗肇锦": 30, "张汉文": 14, "钟乔": 28, "绍英": 110, "黄肇松": 25, "何智辉": 50, "秋华": 547, "苗栗县长": 71, "调借": 31, "分配款": 43, "专户": 3842, "一六九八": 4, "文石": 560, "马公市": 310, "东吉屿": 43, "西屿乡": 84, "拟具": 69, "七美岛": 17, "望安岛": 41, "黑糖": 2733, "丝瓜": 9750, "香瓜": 2146, "丁香鱼": 108, "警广": 73, "村庙": 1100, "佩君": 227, "叶言都": 11, "爱清": 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"林国光": 12, "性爱观": 76, "康士坦丁堡": 8, "发钱寒": 69, "大女人": 1035, "老郑": 539, "姻缘路": 47, "乐而不淫": 104, "明日黄花": 239, "羊玄保": 24, "教龄": 929, "皮包公司": 672, "阿进": 113, "老鼠炮": 5, "矮冬瓜": 126, "互瞪": 12, "飞行史": 28, "势在必得": 283, "溜达": 5644, "接处": 276, "徐阳": 243, "张贞部": 6, "概要图": 21, "太安街": 88, "查算": 35, "川师大": 152, "周介铭": 2, "参观完": 359, "状元楼": 180, "踢落": 73, "罚踢": 26, "林向阳": 22, "布登勃洛克": 42, "狗肚山": 7, "贫地": 17, "晋名峰": 14, "汉治萍铁厂": 2, "毛线沟": 77, "楼区": 245, "一手楼": 207, "电光炮": 55, "怪球": 32, "波道": 184, "石板镇": 290, "李文栋": 3, "玄智": 28, "伸展出来": 46, "沙拉信": 12, "他宁": 78, "顺通": 758, "差瓦立": 34, "萎缩为": 38, "臣那越": 5, "拐入": 240, "拆迁量": 51, "市物价局": 2729, "芨芨": 56, "红柳沟": 81, "污烟": 18, "跨线桥": 1493, "少掉": 390, "刀光剑影": 1314, "争高": 81, "理查逊": 28, "虚荣性": 4, "动力型": 120, "美技": 40, "褓": 160, "满垒": 275, "一查到底": 160, "环博": 26, "新埠": 190, "五十幅": 11, "插座用": 122, "信息性": 102, "小燕姐": 79, "白嘉莉": 52, "待久": 742, "必有得": 142, "扔出去": 1444, "屈打": 22, "李济琛": 21, "谭启秀": 14, "海军界": 16, "惠安涂岭": 3, "彭泽湘": 16, "琼生": 58, "勾联": 23, "独住": 124, "当口": 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"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, "說": 26135, "時候": 5022, "掛": 511, "考据癖": 50, "满头雾水": 32, "聲": 3516, "難": 4764, "寫": 4764, "他們": 3818, "幻视画": 2, "然後": 3999, "給": 7781, "嗎": 3975, "帶": 2953, "顆": 532, "烂糟糟": 17, "最後": 2623, "別人": 1553, "並不": 1403, "真胸": 40, "唬不住": 26, "你們": 2050, "没得说的": 445, "C#": 4316, "錯": 6235, "鎚": 140, "轟": 164, "莫明其妙": 419, "以後": 1712, "爽呆": 132, "缽": 24, "尴尬症": 678, "強大": 249, "來自": 680, "湊": 296, "噁": 405, "按下不表": 379, "之後": 3234, "簡單": 1562, "胡抡": 30, "兴致冲冲": 132, "咣啷": 38, "唰唰唰": 114, "明显显": 23, "满盘皆活": 6, "進行": 1102, "塗": 213, "後面": 609, "結局": 195, "佔": 1399, "腻腻歪歪": 154, "表弟们": 22, "摔走": 8, "脑涨": 169, "選": 4324, "嘗": 454, "滿滿": 82, "屁颠颠": 126, "國際": 760, "乱嘈嘈": 4, "前後": 321, "那麽": 2541, "这麽": 1521, "剪起": 37, "似梦似幻": 18, "怎麽": 3918, "阻力区": 67, "欧阳真": 3, "凸浮": 2, "秃黄": 58, "有无搞错": 36, "挺升": 14, "閤": 245, "哈叭狗": 19, "王八糕子": 3, "二奶车": 27, "扑簌簌": 44, "蝦": 310, "瞄见": 42, "脸红脖子粗": 201, "撞扁": 34, "歇息歇息": 13, "踩准": 91, "徐显强": 2, "遊": 3216, "悔断": 38, "閒": 10423, "清谈类": 6, "蒜你狠": 103, "倦缩": 26, "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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"耐得起": 10, "王采臣": 3, "起得了": 31, "旋过": 17, "蹽": 17, "正正好好": 69, "呕出来": 110, "丢光光": 8, "得啖笑": 5, "中方专家组": 4, "汇总来": 13, "自甘为": 17, "老挨": 16, "唔改": 5, "直乐": 44, "周杭": 2, "寒气袭人": 119, "輓": 16, "潜在力": 24, "环比降": 99, "张我为": 4, "谢恩伟": 17, "闭目塞听": 62, "锋友": 55, "心动神驰": 5, "早育": 131, "安裝": 337, "短线客": 102, "七八百元": 114, "范丝": 3, "闹烘烘": 6, "苏州体育馆": 5, "暮潺潺": 6, "艾怨": 3, "低物价": 62, "青花罐": 35, "摇窗": 65, "意见簿": 37, "难逃劫难": 10, "哧啦哧啦": 5, "进步不了": 37, "溜须": 71, "铜矿厂": 17, "六连拍": 22, "噬脐莫及": 5, "拼掉": 68, "任贵林": 2, "啃书本": 85, "无房者": 35, "藏克家": 26, "湿润润": 44, "几万辆": 32, "与世共存": 2, "好加在": 9, "轴儿": 10, "有取有舍": 39, "棒子面粥": 16, "水渍险": 114, "醒醒定定": 4, "脚趾公": 3, "渦": 49, "大雅大俗": 17, "逼捐": 113, "搬卸": 42, "摺子": 7, "拼上去": 30, "再接再历": 15, "日盛陈": 6, "斗亮": 3, "湧": 1761, "今年六月份": 326, "披金戴银": 23, "千儿八百": 91, "互感体": 2, "怎奈得": 4, "蠔": 275, "防蚊剂": 16, "互抄": 17, "日他娘的": 6, "生拉活扯": 8, "磕绊": 114, "外好莱坞": 4, "马年华": 3, "争论声": 12, "酱赤": 12, "泡沫论": 85, "收活": 13, "洁化": 9, "潮呼呼": 12, "顶胃": 10, "顺逆境": 19, "长江水文局": 2, "稀哩哗啦": 11, 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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 0000000..a2418fe Binary files /dev/null and b/macropodus_images/macropodus_logo.png differ diff --git a/macropodus_preprocess/__init__.py 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)