|
|
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/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学
|
|
|
|
|
|
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`analyse/idf.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 /
|
|
|
|
|
|
* 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
|
|
|
|
|
|
|
|
|
|
|
|
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/
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
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/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学
|
|
|
|
|
|
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 `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
|
|
|
|
|
|
|
|
|
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/
|