文本相似在问答系统中有很重要的应用,如基于知识的问答系统(Knowledge-based QA),基于文档的问答系统(Documen-based QA),以及基于FAQ的问答系统(Community-QA)等。像 对于问题的内容,需要进行相似度匹配,从而选择出与问题最接近,同时最合理的答案。本节介绍 基于Jaccard相似度。
算法描述:两句子分词后词语的交集中词语数与并集中词语数之比。
import os
import jieba
import pickle
import logging
import numpy as np
class StopWords(object):
'''
'''
def __init__(self, stopwords_file=stopwords_file ):
self.stopwords = set( [ word.strip() for word in open(stopwords_file, 'r') ] )
def del_stopwords(self, words):
return [ word for word in words if word not in self.stopwords ]
stop_word = StopWords()
# 是否分词、 及其停用词语
def _seg_word(words, jieba_flag=True, del_stopword=False):
if jieba_flag:
word_list = [stop_word.del_stopwords(words) if del_stopword else word for word in jieba.cut(words)]
else:
word_list = [stop_word.del_stopwords(words) if del_stopword else word for word in words]
return word_list
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