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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

在线演示

http://jiebademo.ap01.aws.af.cm/

(Powered by Appfog)

Python 2.x 下的安装

  • 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

Python 3.x 下的安装

  • 目前master分支是只支持Python2.x 的

  • Python3.x 版本的分支也已经基本可用: https://github.com/fxsjy/jieba/tree/jieba3k

      git clone https://github.com/fxsjy/jieba.git
      git checkout jieba3k
      python setup.py install

Algorithm

  • 基于Trie树结构实现高效的词图扫描,生成句子中汉字所有可能成词情况所构成的有向无环图(DAG)
  • 采用了动态规划查找最大概率路径, 找出基于词频的最大切分组合
  • 对于未登录词,采用了基于汉字成词能力的HMM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参数用来控制是否采用全模式
  • jieba.cut_for_search方法接受一个参数:需要分词的字符串,该方法适合用于搜索引擎构建倒排索引的分词,粒度比较细
  • 注意:待分词的字符串可以是gbk字符串、utf-8字符串或者unicode
  • jieba.cut以及jieba.cut_for_search返回的结构都是一个可迭代的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)  # 精确模式

seg_list = jieba.cut("他来到了网易杭研大厦")  # 默认是精确模式
print ", ".join(seg_list)

seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所,后在日本京都大学深造")  # 搜索引擎模式
print ", ".join(seg_list)

Output:

【全模式】: 我/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学

【精确模式】: 我/ 来到/ 北京/ 清华大学

【新词识别】: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)

【搜索引擎模式】: 小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, 后, 在, 日本, 京都, 大学, 日本京都大学, 深造

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;每一行分三部分,一部分为词语,另一部分为词频,最后为词性(可省略),用空格隔开

  • 范例:

  • "通过用户自定义词典来增强歧义纠错能力" --- https://github.com/fxsjy/jieba/issues/14

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

功能 4) : 词性标注

  • 标注句子分词后每个词的词性,采用和ictclas兼容的标记法

  • 用法示例

      >>> import jieba.posseg as pseg
      >>> words = pseg.cut("我爱北京天安门")
      >>> for w in words:
      ...    print w.word, w.flag
      ...
      我 r
      爱 v
      北京 ns
      天安门 ns

功能 5) : 并行分词

  • 原理:将目标文本按行分隔后,把各行文本分配到多个python进程并行分词,然后归并结果,从而获得分词速度的可观提升

  • 基于python自带的multiprocessing模块,目前暂不支持windows

  • 用法:

    • jieba.enable_parallel(4) # 开启并行分词模式,参数为并行进程数
    • jieba.disable_parallel() # 关闭并行分词模式
  • 例子: https://github.com/fxsjy/jieba/blob/master/test/parallel/test_file.py

  • 实验结果:在4核3.4GHz Linux机器上,对金庸全集进行精确分词,获得了1MB/s的速度,是单进程版的3.3倍。

其他词典

  1. 占用内存较小的词典文件 https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.small

  2. 支持繁体分词更好的词典文件 https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.big

下载你所需要的词典,然后覆盖jieba/dict.txt 即可或者用jieba.set_dictionary('data/dict.txt.big')

模块初始化机制的改变:lazy load (从0.28版本开始)

jieba采用延迟加载,"import jieba"不会立即触发词典的加载,一旦有必要才开始加载词典构建trie。如果你想手工初始jieba,也可以手动初始化。

import jieba
jieba.initialize()  # 手动初始化(可选)

在0.28之前的版本是不能指定主词典的路径的,有了延迟加载机制后,你可以改变主词典的路径:

jieba.set_dictionary('data/dict.txt.big')

例子: https://github.com/fxsjy/jieba/blob/master/test/test_change_dictpath.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

常见问题

1)模型的数据是如何生成的?https://github.com/fxsjy/jieba/issues/7

2)这个库的授权是? https://github.com/fxsjy/jieba/issues/2

更多问题请点击:https://github.com/fxsjy/jieba/issues?sort=updated&state=closed

Change Log

https://github.com/fxsjy/jieba/blob/master/Changelog

jieba

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

Features

  • Support three types of segmentation mode:
    1. Accurate Mode, attempt to cut the sentence into the most accurate segmentation, which is suitable for text analysis;
    1. Full Mode, break the words of the sentence into words scanned
    1. Search Engine Mode, based on the Accurate Mode, with an attempt to cut the long words into several short words, which can enhance the recall rate

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 HMM-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.
  • jieba.cut_for_search accpets only on parameter: the string that requires segmentation, and it will cut the sentence into short words

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)

seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所,后在日本京都大学深造")  # 搜索引擎模式
print ", ".join(seg_list)

Output:

[Full Mode]: 我/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学

[Accurate Mode]: 我/ 来到/ 北京/ 清华大学

[Unknown Words Recognize] 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)

[Search Engine Mode]: 小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, 后, 在

, 日本, 京都, 大学, 日本京都大学, 深造

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 analyse/idf.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

Using Other Dictionaries

It is possible to supply Jieba with your own custom dictionary, and there are also two dictionaries readily available for download:

  1. You can employ a smaller dictionary for a smaller memory footprint: https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.small

  2. There is also a bigger file that has better support for traditional characters (繁體): https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.big

By default, an in-between dictionary is used, called dict.txt and included in the distribution.

In either case, download the file you want first, and then call jieba.set_dictionary('data/dict.txt.big') or just replace the existing dict.txt.

Initialization

By default, Jieba employs lazy loading to only build the trie once it is necessary. This takes 1-3 seconds once, after which it is not initialized again. If you want to initialize Jieba manually, you can call:

import jieba
jieba.initialize()  # (optional)

You can also specify the dictionary (not supported before version 0.28) :

jieba.set_dictionary('data/dict.txt.big')

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://jiebademo.ap01.aws.af.cm/

(Powered by Appfog)

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