结巴中文分词
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README.md

jieba

"结巴"中文分词:做最好的Python中文分词组件 "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.

  • Scroll down for English documentation.

Feature

  • 支持两种分词模式:
  • 1)默认模式,试图将句子最精确地切开,适合文本分析;
  • 2)全模式,把句子中所有的可以成词的词语都扫描出来,适合搜索引擎。

Usage

  • 全自动安装:easy_install jieba 或者 pip install jieba
  • 半自动安装:先下载http://pypi.python.org/pypi/jieba/ ,解压后运行python setup.py install
  • 手动安装:将jieba目录放置于当前目录或者site-packages目录
  • 通过import jieba 来引用 (第一次import时需要构建Trie树,需要几秒时间)

Algorithm

  • 基于Trie树结构实现高效的词图扫描,生成句子中汉字构成的有向无环图(DAG)
  • 采用了记忆化搜索实现最大概率路径的计算, 找出基于词频的最大切分组合
  • 对于未登录词,采用了基于汉字位置概率的模型,使用了Viterbi算法

功能 1):分词

  • jieba.cut方法接受两个输入参数: 1) 第一个参数为需要分词的字符串 2)cut_all参数用来控制分词模式
  • 待分词的字符串可以是gbk字符串、utf-8字符串或者unicode
  • jieba.cut返回的结构是一个可迭代的generator,可以使用for循环来获得分词后得到的每一个词语(unicode),也可以用list(jieba.cut(...))转化为list

代码示例( 分词 )

#encoding=utf-8
import jieba

seg_list = jieba.cut("我来到北京清华大学",cut_all=True)
print "Full Mode:", "/ ".join(seg_list) #全模式

seg_list = jieba.cut("我来到北京清华大学",cut_all=False)
print "Default Mode:", "/ ".join(seg_list) #默认模式

seg_list = jieba.cut("他来到了网易杭研大厦")
print ", ".join(seg_list)

Output:

Full Mode: 我/ 来/ 来到/ 到/ 北/ 北京/ 京/ 清/ 清华/ 清华大学/ 华/ 华大/ 大/ 大学/ 学

Default Mode: 我/ 来到/ 北京/ 清华大学

他, 来到, 了, 网易, 杭研, 大厦    (此处,“杭研”并没有在词典中,但是也被Viterbi算法识别出来了)

功能 2) :添加自定义词典

  • 开发者可以指定自己自定义的词典,以便包含jieba词库里没有的词。虽然jieba有新词识别能力,但是自行添加新词可以保证更高的正确率
  • 用法: jieba.load_userdict(file_name) # file_name为自定义词典的路径
  • 词典格式和dict.txt一样,一个词占一行;每一行分为两部分,一部分为词语,另一部分为词频,用空格隔开
  • 范例:

    云计算 5
    李小福 2
    创新办 3
    
    之前: 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /
    
    加载自定义词库后: 李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
    

功能 3) :关键词提取

  • jieba.analyse.extract_tags(sentence,topK) #需要先import jieba.analyse
  • setence为待提取的文本
  • topK为返回几个TF/IDF权重最大的关键词,默认值为20

代码示例 (关键词提取)

https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py

分词速度

  • 1.5 MB / Second in Full Mode
  • 400 KB / Second in Default Mode
  • Test Env: Intel(R) Core(TM) i7-2600 CPU @ 3.4GHz;《围城》.txt

在线演示

http://209.222.69.242:9000/

jieba

"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.

Features

  • Support two types of segmentation mode:
  • 1) Default mode, attempt to cut the sentence into the most accurate segmentation, which is suitable for text analysis;
  • 2) Full mode, break the words of the sentence into words scanned, which is suitable for search engines.

Usage

  • Fully automatic installation: easy_install jieba or pip install jieba
  • Semi-automatic installation: Download http://pypi.python.org/pypi/jieba/ , after extracting run python setup.py install
  • Manutal installation: place the jieba directory in the current directory or python site-packages directory.
  • Use import jieba to import, which will first build the Trie tree only on first import (takes a few seconds).

Algorithm

  • Based on the Trie tree structure to achieve efficient word graph scanning; sentences using Chinese characters constitute a directed acyclic graph (DAG)
  • Employs memory search to calculate the maximum probability path, in order to identify the maximum tangential points based on word frequency combination
  • For unknown words, the character position probability-based model is used, using the Viterbi algorithm

Function 1): cut

  • The jieba.cut method accepts to input parameters: 1) the first parameter is the string that requires segmentation, and the 2) second parameter is cut_all, a parameter used to control the segmentation pattern.
  • jieba.cut returned structure is an iterative generator, where you can use a for loop to get the word segmentation (in unicode), or list(jieba.cut( ... )) to create a list.

Code example: segmentation

#encoding=utf-8
import jieba

seg_list = jieba.cut("我来到北京清华大学",cut_all=True)
print "Full Mode:", "/ ".join(seg_list) #全模式

seg_list = jieba.cut("我来到北京清华大学",cut_all=False)
print "Default Mode:", "/ ".join(seg_list) #默认模式

seg_list = jieba.cut("他来到了网易杭研大厦")
print ", ".join(seg_list)

Output:

Full Mode: 我/ 来/ 来到/ 到/ 北/ 北京/ 京/ 清/ 清华/ 清华大学/ 华/ 华大/ 大/ 大学/ 学

Default Mode: 我/ 来到/ 北京/ 清华大学

他, 来到, 了, 网易, 杭研, 大厦    (In this case, "杭研" is not in the dictionary, but is identified by the Viterbi algorithm)

Function 2): Add a custom dictionary

  • Developers can specify their own custom dictionary to include in the jieba thesaurus. jieba has the ability to identify new words, but adding your own new words can ensure a higher rate of correct segmentation.
  • Usage: jieba.load_userdict(file_name) # file_name is a custom dictionary path
  • The dictionary format is the same as that of dict.txt: one word per line; each line is divided into two parts, the first is the word itself, the other is the word frequency, separated by a space
  • Example:

    云计算 5
    李小福 2
    创新办 3
    
    之前: 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /
    
    加载自定义词库后: 李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
    

Function 3): Keyword Extraction

  • jieba.analyse.extract_tags(sentence,topK) # needs to first import jieba.analyse
  • setence: the text to be extracted
  • topK: To return several TF / IDF weights for the biggest keywords, the default value is 20

Code sample (keyword extraction)

https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py

Segmentation speed

  • 1.5 MB / Second in Full Mode
  • 400 KB / Second in Default Mode
  • Test Env: Intel(R) Core(TM) i7-2600 CPU @ 3.4GHz;《围城》.txt

Online demo

http://209.222.69.242:9000/