使用word2vec对中文维基百科数据进行处理

一.下载中文维基百科数据https://dumps.wikimedia.org/zhwiki/
并使用gensim中的wikicorpus解析提取xml中的内容

二.利用opencc繁体转简体

三.利用jieba对转换后的文本进行分词，去停词

四.利用gensim中的word2vec训练分词后的文本

五.测试

python代码如下：

#!/user/bin/python
#coding:utf-8
__author__ = 'yan.shi'
from gensim.corpora import WikiCorpus
import opencc
import jieba
import codecs
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
import multiprocessing

'''
读取中文wiki语料库，并解析提取xml中的内容
'''
def dataprocess():
    space=b' '
    i=0
    output=open('E:\zhwiki-articles.txt','wb')
    wiki=WikiCorpus('E:\zhwiki-latest-pages-articles.xml.bz2',lemmatize=False,dictionary={})
    for text in wiki.get_texts():
        output.write(space.join(text)+b'\n')
        i=i+1
        if(i%10000==0):
            print('Saved '+str(i)+' articles')
    output.close()
    print('Finished Saved '+str(i)+' articles')

'''
加载停用词表
'''
def createstoplist(stoppath):
    print('load stopwords...')
    stoplist=[line.strip() for line in codecs.open(stoppath,'r',encoding='utf-8').readlines()]
    stopwords={}.fromkeys(stoplist)
    return stopwords

'''
过滤英文
'''
def isAlpha(word):
    try:
        return word.encode('ascii').isalpha()
    except UnicodeEncodeError:
        return False

'''
opencc繁体转简体，jieba中文分词
'''
def trans_seg():
    stopwords=createstoplist('E:\stopwords.txt')
    cc=opencc.OpenCC('t2s')
    i=0
    with codecs.open('E:\zhwiki-segment.txt','w','utf-8') as wopen:
        print('开始...')
        with codecs.open('E:\wiki-utf8.txt','r','utf-8') as ropen:
            while True:
                line=ropen.readline().strip()
                i+=1
                print('line '+str(i))
                text=''
                for char in line.split():
                    if isAlpha(char):
                        continue
                    char=cc.convert(char)
                    text+=char
                words=jieba.cut(text)
                seg=''
                for word in words:
                    if word not in stopwords:
                        if len(word)>1 and isAlpha(word)==False: #去掉长度小于1的词和英文
                            if word !='\t':
                                seg+=word+' '
                wopen.write(seg+'\n')
    print('结束!')

'''
利用gensim中的word2vec训练词向量
'''
def word2vec():
    print('Start...')
    rawdata='G:\python workspace\zhwiki-segment.txt'
    modelpath='G:\python workspace\modeldata.model'
    #vectorpath='E:\word2vec\vector'
    model=Word2Vec(LineSentence(rawdata),size=400,window=5,min_count=5,workers=multiprocessing.cpu_count())#参数说明，gensim函数库的Word2Vec的参数说明
    model.save(modelpath)
    #model.wv.save_word2vec_format(vectorpath,binary=False)
    print("Finished!")

def wordsimilarity():
    model=Word2Vec.load('E:\word2vec\modeldata.model')
    semi=''
    try:
        semi=model.most_similar('日本'.decode('utf-8'),topn=10)#python3以上就不需要decode
    except KeyError:
        print('The word not in vocabulary!')

    #print(model[u'日本'])#打印词向量
    for term in semi:
        print('%s,%s' %(term[0],term[1]))

if __name__=='__main__':
    #dataprocess()
    #trans_seg()
    #word2vec()
    wordsimilarity()

日本的相关词：