唐诗掠影:基于词移距离(Word Mover's Distance)的唐诗诗句匹配实践


词移距离(Word Mover's Distance)是在词向量的基础上发展而来的用来衡量文档相似性的度量。
 
词移距离的具体介绍参考 http://blog.csdn.net/qrlhl/article/details/78512598   或网上的其他资料
 
此处,用词移距离来衡量唐诗诗句的相关性。为什么用唐诗?因为全唐诗的txt很容易获取,随便一搜就可以下载了。全唐诗txt链接:https://files.cnblogs.com/files/combfish/%E5%85%A8%E5%94%90%E8%AF%97.zip。
 
步骤:
1. 预处理语料集: 唐诗的断句分词,断句基于标点符号,分词依靠结巴分词
2. gensim训练词向量模型与wmd相似性模型
3. 查询
 
代码:
import jieba
from nltk import word_tokenize
from nltk.corpus import stopwords
from time import time
start_nb = time()
import logging

print(20*'*','loading data',40*'*')
f=open('全唐诗.txt',encoding='utf-8')
lines=f.readlines()
corpus=[]
documents=[]
useless=[',','.','(',')','!','?','\'','\"',':','<','>',
         ',', '。', '(', ')', '!', '?', '’', '“',':','《','》','[',']','【','】']
for each in lines:
    each=each.replace('\n','')
    each.replace('-','')
    each=each.strip()
    each=each.replace(' ','')
    if(len(each)>3):
        if(each[0]!='卷'):
            documents.append(each)
            each=list(jieba.cut(each))
            text=[w for w in each if not w in useless]
            corpus.append(text)

print(len(corpus))

print(20*'*','trainning models',40*'*')
from gensim.models import Word2Vec
model = Word2Vec(corpus, workers=3, size=100)

# Initialize WmdSimilarity.
from gensim.similarities import WmdSimilarity
num_best = 10
instance = WmdSimilarity(corpus, model, num_best=10)

print(20*'*','testing',40*'*')
while True:
    sent = input('输入查询语句: ')
    sent_w = list(jieba.cut(sent))
    query = [w for w in sent_w if not w in useless]

    sims = instance[query]  # A query is simply a "look-up" in the similarity class.

    # Print the query and the retrieved documents, together with their similarities.
    print('Query:')
    print(sent)
    for i in range(num_best):
        print
        print('sim = %.4f' % sims[i][1])
        print(documents[sims[i][0]])

  

结果:从结果kan
 
 
 
 
 
 
 
 
 

<wiz_tmp_tag id="wiz-table-range-border" contenteditable="false" style="display: none;">






免责声明!

本站转载的文章为个人学习借鉴使用,本站对版权不负任何法律责任。如果侵犯了您的隐私权益,请联系本站邮箱yoyou2525@163.com删除。



 
粤ICP备18138465号  © 2018-2025 CODEPRJ.COM