结巴分词

#!coding: utf-8

import jieba
import jieba.posseg as pseg
import jieba.analyse as anal
from optparse import OptionParser

usage = "usage: python %prog [--tfidf topK] [--textr topK]";
parser = OptionParser(usage);
parser.add_option("--tag", dest="tag", action="store_true");
parser.add_option("--fast", dest="fast", action="store_true");
parser.add_option("--tfidf", dest="tfidf");
parser.add_option("--textr", dest="textr");
opt,args = parser.parse_args();


txt = "支持三种分词模式： 精确模式，试图将句子最精确地切开，适合文本分析； 全模式，把句子中所有的可以成词的词语都扫描出来, 速度非常快，但是不能解决歧义； 搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。  支持繁体分词 支持自定义词典 MIT 授权协议 在线演示";


#multiprocess
if opt.fast:
    jieba.enable_parallel(10);



#define word-dict
jieba.add_word("全模式");
jieba.suggest_freq(("协","议"), True) ;



#generator
print "/".join(jieba.cut(txt));


#list
print "/".join(jieba.lcut(txt));


#search mode
print "/".join(jieba.cut_for_search(txt));


#get word‘s position
res = jieba.tokenize(txt.decode("utf-8"));
#res = jieba.tokenize(txt.decode("utf-8"), mode="search"); #search mode
print "word\t\tstart\t\tend";
for tk in res:
    print("%s\t\t %d \t\t %d" % (tk[0],tk[1],tk[2]));



#tagging word
if opt.tag:
    for w,k in pseg.cut(txt):
        print w+"("+k+")",



#tfidf sort keyword
if opt.tfidf:
    topK =  int(opt.tfidf);
    tags = anal.extract_tags(txt, topK, withWeight=True);

    for word,weight in tags:
        print word,weight



#textrank sort keyword
if opt.textr:
    topk = int(opt.textr);
    tags = anal.textrank(txt, topk, withWeight=True);

    for word,weight in tags:
        print word,weight;