python 文本相似度_Doc2Vec,Word2Vec文本相似度 初體驗。

接上篇 ：

import jieba

all_list = jieba.cut(xl['工作内容'][0:6],cut_all=True)

print(all_list)

every_one = xl['工作内容'].apply(lambda x:jieba.cut(x))

import traceback

def filtered_punctuations(token_list):

try:

punctuations = [' ', '\n', '\t', ',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%','：',

'/','\xa0','。','；','、']

token_list_without_punctuations = [word for word in token_list

if word not in punctuations]

#print "[INFO]: filtered_punctuations is finished!"

return token_list_without_punctuations

except Exception as e:

print (traceback.print_exc())

from gensim.models import Doc2Vec,Word2Vec

import gensim

def list_crea(everyone):

list_word = []

for k in everyone:

fenci= filtered_punctuations(k)

list_word.append(fenci)

return list_word

aa_word = list_crea(every_one)

print(type(aa_word))

#aa_word 是 個 嵌套的list [[1,2,3], [4,5,6], [7,8,9]]

model = Word2Vec(aa_word, min_count=1) # 訓練模型，參考英文官網，在上面

say_vector = model['java'] # get vector for word

model.similarity('計算', '計算機')