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This article shares with you three commonly used Python Chinese word segmentation tools, which have certain reference value. Friends in need can refer to
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# -*- coding: UTF-8 -*- import os import codecs import jieba seg_list = jieba.cut('邓超,1979年出生于江西南昌,中国内地男演员、电影导演、投资出品人、互联网投资人。') f1 = codecs.open("d2w_ltp.txt","w") print "/".join(seg_list) for i in seg_list: f1.write(i.encode("utf-8")) f1.write(str(" "))
Effect:
邓超/,/1979/年出/生于/江西/南昌/,/中国/内地/男演员/、/电影/导演/、/投资/出品人/、/互联网/投资人/。
This includes the stuttering participle and the form of writing to the file
It is worth noting that the character encoding derived from stuttering word segmentation is 'Unicode' encoding. We need to convert unicode -> utf-8
r = open('text_no_seg.txt','r') list_senten = [] sentence = '邓超,1979年出生于江西南昌,中国内地男演员、电影导演、投资出品人、互联网投资人。' for i in seg(sentence): list_senten.append(i[0]) print "/".join(list_senten) f1 = codecs.open("d2w_ltp.txt","w") for i in seg(sentence): f1.write(i[0]) f1.write(str(" "))Effect:
邓超/,/1979年/出生/于/江西/南昌/,/中国/内地/男/演员/、/电影/导演/、/投资/出品/人/、/互联网/投资人/。Of course, NLPIR also has a very good effect in named entity recognition:
邓超 nr , wd 1979年 t 出生 vi 于 p 江西 ns 南昌 ns , wd 中国 ns 内地 s 男 b 演员 n 、 wn 电影 n 导演 n 、 wn 投资 n 出品 vi 人 n 、 wn 互联网 n 投资人 n 。 wj
# -*- coding: UTF-8 -*- import os import codecs from pyltp import Segmentor #分词 def segmentor(sentence): segmentor = Segmentor() # 初始化实例 segmentor.load('ltp_data/cws.model') # 加载模型 words = segmentor.segment(sentence) # 分词 words_list = list(words) segmentor.release() # 释放模型 return words_list f1 = codecs.open("d2w_ltp.txt","w") sentence = '邓超,1979年出生于江西南昌,中国内地男演员、电影导演、投资出品人、互联网投资人。' print "/".join(segmentor(sentence)) for i in segmentor(sentence): f1.write(i) f1.write(str(" "))
邓/超/,/1979年/出生/于/江西/南昌/,/中国/内地/男/演员/、/电影/导演/、/投资/出品人/、/互联网/投资人/。
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