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[文本语义相似] 基于bow的余弦距离

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bow的余弦距离计算相似度。

 

本节将介绍两种实现:基于sklearn  和 基于gensim

 

基于sklearn的方式如下:

 

import os
import jieba
import pickle
import logging
import numpy as np
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer 

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_list, 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.

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