添加自动问答模块
This commit is contained in:
liu huanyong
parent
222ade9217
commit
9f9cbf7570
@ -20,12 +20,24 @@
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@ -35,25 +47,13 @@
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@ -73,8 +73,9 @@
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<option value="$PROJECT_DIR$/question_classify_train.py" />
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@ -141,6 +142,20 @@
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@ -162,7 +177,7 @@
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<component name="RunManager" selected="Python.crime_qa">
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<option name="PARENT_ENVS" value="true" />
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@ -197,6 +212,23 @@
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<option name="SHOW_COMMAND_LINE" value="false" />
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<method />
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</configuration>
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<configuration default="false" name="crime_qa" type="PythonConfigurationType" factoryName="Python" temporary="true">
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<option name="INTERPRETER_OPTIONS" value="" />
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<option name="ADD_CONTENT_ROOTS" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<module name="CrimeKgAssistant" />
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@ -342,14 +374,16 @@
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@ -375,7 +409,7 @@
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@ -433,26 +467,6 @@
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53
README.md
53
README.md
@ -105,9 +105,58 @@ Crime assistant including crime type prediction and crime consult service based
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question_type: 婚姻家庭 0.9993444085121155
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# 法务咨询自动问答
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运行 python crime_qa.py
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question:朋友欠钱不还咋办
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answers: ['欠款金额是多少 ', '多少钱呢', '律师费诉讼费都非常少都很合理，一定要起诉。', '大概金额多少？', '需要看标的额和案情复杂程度，建议细致面谈']
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*******************************************************
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question:昨天把人家车刮了,要赔多少
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answers: ['您好，建议协商处理，如果对方告了你们，就只能积极应诉了。', '您好，建议尽量协商处理，协商不成可起诉']
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*******************************************************
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question:最近丈夫经常家暴,我受不了了
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answers: ['报警要求追究刑事责任。', '您好，建议起诉离婚并请求补偿。', '你好！可以起诉离婚，并主张精神损害赔偿。']
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*******************************************************
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question:毕业生拿了户口就跑路可以吗
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answers: 您好,对于此类问题,您可以咨询公安部门
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*******************************************************
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question:孩子离家出走,怎么找回来
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answers: ['孩子父母没有结婚，孩子母亲把孩子带走了？这样的话可以起诉要求抚养权的。毕竟母亲也是孩子的合法监护人，报警警察一般不受理。']
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*******************************************************
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question:村霸把我田地给占了,我要怎么起诉
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answers: ['可以向上级主管部门投诉解决', '您好，您可以及时向土地管理部门投诉的！', '对方侵权，可以向法院起诉。', '你好，对方侵权，可以向法院起诉。', '你好，可起诉处理，一、当事人起诉，首先应提交起诉书，并按对方当事人人数提交相应份数的副本。当事人是公民的，应写明双方当事人的姓名、性别、年龄、籍贯、住址;当事人是单位的，应写明单位名称、地址、法定代表人或负责人姓名。起诉书正文应写明请求事项和起诉事实、理由，尾部须署名或盖公章。二、根据"谁主张谁举证"原则，原告向法院起诉应提交下列材料：1、原告主体资格的材料。如居民身份证、户口本、护照、港澳同胞回乡证、结婚证等证据的原件和复印件;企业单位作为原告的应提交营业执照、商业登记证明等材料的复印件。2、证明原告诉讼主张的证据。如合同、协议、债权文书(借条、欠条等)、收发货凭证、往来信函等。', '您好，起诉维权。', '您好，可以起诉解决。']
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*******************************************************
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question:售卖危违禁物品,有什么风险
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answers: ['没什么']
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*******************************************************
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question:找不到女朋友啊..
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answers: 您好,对于此类问题,您可以咨询公安部门
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*******************************************************
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question:我要离婚
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answers: ['现在就可向法院起诉离婚。', '不需要分开两年起诉离婚。感情完全破裂就可以提起诉讼离婚。', '你可以直接起诉离婚', '直接起诉']
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*******************************************************
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question:醉驾,要坐牢吗
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answers: ['要负刑事责任很可能坐牢', '由警方处理.,']
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*******************************************************
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question:你好，我向大学提出退学申请，大学拒绝，理由是家长不同意。我该怎么办？
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answers: ['自己可决定的 ']
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*******************************************************
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question:请问在上班途中，出车祸我的责任偏大属于工伤吗？
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answers: ['属于工伤']
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*******************************************************
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question:结婚时女方拿了彩礼就逃了能要回来吗
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answers: ['可以要求退还彩礼。，']
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*******************************************************
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question:房产证上是不是一定要写夫妻双方姓名
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answers: ['可以不填，即使一个人的名字，婚后买房是共同财产。', '不是必须的', '可以写一方名字，对方公证，证明该房产系你一人财产', '你好，不是必须']
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*******************************************************
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question:儿女不履行赡养义务是不是要判刑
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answers: ['什么情况了？']
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*******************************************************
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question:和未成年人发生关系,需要坐牢吗
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answers: ['女孩子在发生关系的时候是否满14周岁，如果是且自愿就不是犯罪', '你好，如果是双方愿意的情况下是不犯法的。', '发生性关系时已满十四岁并且是自愿的依法律规定不构成强奸罪，不构成犯罪的。', '若是自愿，那就没什么可说了。', '双方愿意不犯法', '你好 如果是自愿的 不犯法 ', '自愿的就没事']
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*******************************************************
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question:撞死人逃跑要怎么处理
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answers: ['等待警察处理。，']
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# 罪刑知识查询
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@ -88,6 +88,8 @@ class CrimeClassify(object):
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return label
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def test():
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handler = CrimeClassify()
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while(1):
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||||
97
crime_qa.py
97
crime_qa.py
@ -8,15 +8,20 @@ import os
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import time
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import json
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from elasticsearch import Elasticsearch
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from elasticsearch.helpers import bulk
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import pymongo
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import numpy as np
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import jieba.posseg as pseg
|
||||
|
||||
class CrimeQA:
|
||||
def __init__(self):
|
||||
self._index = "crime_data"
|
||||
self.es = Elasticsearch([{"host": "127.0.0.1", "port": 9200}])
|
||||
self.doc_type = "crime"
|
||||
|
||||
cur = '/'.join(os.path.abspath(__file__).split('/')[:-1])
|
||||
self.embedding_path = os.path.join(cur, 'embedding/word_vec_300.bin')
|
||||
self.embdding_dict = self.load_embedding(self.embedding_path)
|
||||
self.embedding_size = 300
|
||||
self.min_score = 0.4
|
||||
self.min_sim = 0.8
|
||||
|
||||
'''根据question进行事件的匹配查询'''
|
||||
def search_specific(self, value, key="question"):
|
||||
@ -43,16 +48,92 @@ class CrimeQA:
|
||||
answers.append(answer_dict)
|
||||
return answers
|
||||
|
||||
|
||||
'''加载词向量'''
|
||||
def load_embedding(self, embedding_path):
|
||||
embedding_dict = {}
|
||||
count = 0
|
||||
for line in open(embedding_path):
|
||||
line = line.strip().split(' ')
|
||||
if len(line) < 300:
|
||||
continue
|
||||
wd = line[0]
|
||||
vector = np.array([float(i) for i in line[1:]])
|
||||
embedding_dict[wd] = vector
|
||||
count += 1
|
||||
if count%10000 == 0:
|
||||
print(count, 'loaded')
|
||||
print('loaded %s word embedding, finished'%count, )
|
||||
return embedding_dict
|
||||
|
||||
|
||||
'''对文本进行分词处理'''
|
||||
def seg_sent(self, s):
|
||||
wds = [i.word for i in pseg.cut(s) if i.flag[0] not in ['x', 'u', 'c', 'p', 'm', 't']]
|
||||
return wds
|
||||
|
||||
'''基于wordvector，通过lookup table的方式找到句子的wordvector的表示'''
|
||||
def rep_sentencevector(self, sentence, flag='seg'):
|
||||
if flag == 'seg':
|
||||
word_list = [i for i in sentence.split(' ') if i]
|
||||
else:
|
||||
word_list = self.seg_sent(sentence)
|
||||
embedding = np.zeros(self.embedding_size)
|
||||
sent_len = 0
|
||||
for index, wd in enumerate(word_list):
|
||||
if wd in self.embdding_dict:
|
||||
embedding += self.embdding_dict.get(wd)
|
||||
sent_len += 1
|
||||
else:
|
||||
continue
|
||||
return embedding/sent_len
|
||||
|
||||
|
||||
'''计算问句与库中问句的相似度,对候选结果加以二次筛选'''
|
||||
def similarity_cosine(self, vector1, vector2):
|
||||
cos1 = np.sum(vector1*vector2)
|
||||
cos21 = np.sqrt(sum(vector1**2))
|
||||
cos22 = np.sqrt(sum(vector2**2))
|
||||
similarity = cos1/float(cos21*cos22)
|
||||
if similarity == 'nan':
|
||||
return 0
|
||||
else:
|
||||
return similarity
|
||||
|
||||
'''问答主函数'''
|
||||
def search_main(self, question):
|
||||
candi_answers = self.search_es(question)
|
||||
for candi in candi_answers:
|
||||
print(candi)
|
||||
|
||||
question_vector = self.rep_sentencevector(question,flag='noseg')
|
||||
answer_dict = {}
|
||||
for indx, candi in enumerate(candi_answers):
|
||||
candi_question = candi['sim_question']
|
||||
score = candi['score']/100
|
||||
candi_vector = self.rep_sentencevector(candi_question, flag='noseg')
|
||||
sim = self.similarity_cosine(question_vector, candi_vector)
|
||||
if sim < self.min_sim:
|
||||
continue
|
||||
final_score = (score + sim)/2
|
||||
if final_score < self.min_score:
|
||||
continue
|
||||
answer_dict[indx] = final_score
|
||||
if answer_dict:
|
||||
answer_dict = sorted(answer_dict.items(), key=lambda asd:asd[1], reverse=True)
|
||||
final_answer = candi_answers[answer_dict[0][0]]['answers']
|
||||
else:
|
||||
final_answer = '您好,对于此类问题,您可以咨询公安部门'
|
||||
#
|
||||
# for i in answer_dict:
|
||||
# answer_indx = i[0]
|
||||
# score = i[1]
|
||||
# print(i, score, candi_answers[answer_indx])
|
||||
# print('******'*6)
|
||||
return final_answer
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
handler = CrimeQA()
|
||||
question = '最近买了一把枪,会犯什么罪?'
|
||||
handler.search_main(question)
|
||||
while(1):
|
||||
question = input('question:')
|
||||
final_answer = handler.search_main(question)
|
||||
print('answers:', final_answer)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user