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.
特点
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
http://jiebademo.ap01.aws.af.cm/
(Powered by Appfog)
网站代码:/fxsjy/jiebademo
安装说明
代码对 Python 2/3 均兼容
全自动安装:easy_install jieba
或者pip install jieba
/pip3 install jieba
半自动安装:先下载 /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
算法
基于前缀词典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 算法主要功能
1) :分词
jieba.cut
方法接受三个输入参数: 需要分词的字符串;cut_all 参数用来控制是否采用全模式;HMM 参数用来控制是否使用 HMM 模型jieba.cut_for_search
方法接受两个参数:需要分词的字符串;是否使用 HMM 模型。该方法适合用于搜索引擎构建倒排索引的分词,粒度比较细待分词的字符串可以是 unicode 或 UTF-8 字符串、GBK 字符串。注意:不建议直接输入 GBK 字符串,可能无法预料地错误解码成 UTF-8jieba.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-8import jiebaseg_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))
输出:
【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
2) :添加自定义词典
载入词典
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(可省略),最后为词性(可省略),用空格隔开词频可省略,使用计算出的能保证分出该词的词频
范例:
自定义词典:/fxsjy/jieba/blob/master/test/userdict.txt
用法示例:/fxsjy/jieba/blob/master/test/test_userdict.py
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 /
调整词典
使用add_word(word, freq=None, tag=None)
和del_word(word)
可在程序中动态修改词典。使用suggest_freq(segment, tune=True)
可调节单个词语的词频,使其能(或不能)被分出来。
注意:自动计算的词频在使用 HMM 新词发现功能时可能无效。
代码示例:
>>> print('/'.join(jieba.cut('如果放到post中将出错。', HMM=False)))如果/放到/post/中将/出错/。>>> jieba.suggest_freq(('中', '将'), True)494>>> print('/'.join(jieba.cut('如果放到post中将出错。', HMM=False)))如果/放到/post/中/将/出错/。>>> print('/'.join(jieba.cut('「台中」正确应该不会被切开', HMM=False)))「/台/中/」/正确/应该/不会/被/切开>>> jieba.suggest_freq('台中', True)69>>> print('/'.join(jieba.cut('「台中」正确应该不会被切开', HMM=False)))「/台中/」/正确/应该/不会/被/切开
“通过用户自定义词典来增强歧义纠错能力” — /fxsjy/jieba/issues/14
3) :关键词提取
jieba.analyse.extract_tags(sentence,topK,withWeight) #需要先import jieba.analyse
sentence 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 20withWeight 为是否一并返回关键词权重值,默认值为 False代码示例 (关键词提取)
/fxsjy/jieba/blob/master/test/extract_tags.py
关键词提取所使用逆向文件频率(IDF)文本语料库可以切换成自定义语料库的路径
用法: jieba.analyse.set_idf_path(file_name) # file_name为自定义语料库的路径自定义语料库示例:/fxsjy/jieba/blob/master/extra_dict/idf.txt.big用法示例:/fxsjy/jieba/blob/master/test/extract_tags_idfpath.py
关键词提取所使用停止词(Stop Words)文本语料库可以切换成自定义语料库的路径
用法: jieba.analyse.set_stop_words(file_name) # file_name为自定义语料库的路径自定义语料库示例:/fxsjy/jieba/blob/master/extra_dict/stop_words.txt用法示例:/fxsjy/jieba/blob/master/test/extract_tags_stop_words.py
关键词一并返回关键词权重值示例
用法示例:/fxsjy/jieba/blob/master/test/extract_tags_with_weight.py
基于TextRank算法的关键词抽取实现
算法论文: TextRank: Bringing Order into Texts
基本思想:
将待抽取关键词的文本进行分词以固定窗口大小(我选的5,可适当调整),词之间的共现关系,构建图计算图中节点的PageRank,注意是无向带权图基本使用:
jieba.analyse.textrank(raw_text)
示例结果:
来自__main__
的示例结果:
吉林 1.0欧亚 0.864834432786置业 0.553465925497实现 0.520660869531收入 0.379699688954增资 0.355086023683子公司 0.349758490263全资 0.308537396283城市 0.306103738053商业 0.304837414946
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('%s %s' % (w.word, w.flag))...我 r爱 v北京 ns天安门 ns
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:/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。
6) : Tokenize:返回词语在原文的起始位置
注意,输入参数只接受 unicode默认模式result = jieba.tokenize(u'永和服装饰品有限公司')for tk in result:print("word %s\t\t start: %d \t\t end:%d" % (tk[0],tk[1],tk[2]))
word 永和start: 0end:2word 服装start: 2end:4word 饰品start: 4end:6word 有限公司 start: 6end:10
搜索模式
result = jieba.tokenize(u'永和服装饰品有限公司',mode='search')for tk in result:print("word %s\t\t start: %d \t\t end:%d" % (tk[0],tk[1],tk[2]))
word 永和start: 0end:2word 服装start: 2end:4word 饰品start: 4end:6word 有限start: 6end:8word 公司start: 8end:10word 有限公司 start: 6end:10
7) : ChineseAnalyzer for Whoosh 搜索引擎
引用:from jieba.analyse import ChineseAnalyzer
用法示例:/fxsjy/jieba/blob/master/test/test_whoosh.py8) : 命令行分词
使用示例:cat news.txt | python -m jieba > cut_result.txt
命令行选项(翻译):
使用: python -m jieba [options] filename结巴命令行界面。固定参数:filename 输入文件可选参数:-h, --help 显示此帮助信息并退出-d [DELIM], --delimiter [DELIM]使用 DELIM 分隔词语,而不是用默认的' / '。若不指定 DELIM,则使用一个空格分隔。-D DICT, --dict DICT 使用 DICT 代替默认词典-u USER_DICT, --user-dict USER_DICT使用 USER_DICT 作为附加词典,与默认词典或自定义词典配合使用-a, --cut-all 全模式分词-n, --no-hmm不使用隐含马尔可夫模型-q, --quiet 不输出载入信息到 STDERR-V, --version 显示版本信息并退出如果没有指定文件名,则使用标准输入。
--help
选项输出:
$> python -m jieba --helpusage: python -m jieba [options] filenameJieba command line interface.positional arguments:filename input fileoptional arguments:-h, --help show this help message and exit-d [DELIM], --delimiter [DELIM]use DELIM instead of ' / ' for word delimiter; or aspace if it is used without DELIM-D DICT, --dict DICT use DICT as dictionary-u USER_DICT, --user-dict USER_DICTuse USER_DICT together with the default dictionary orDICT (if specified)-a, --cut-all full pattern cutting-n, --no-hmmdon't use the Hidden Markov Model-q, --quiet don't print loading messages to stderr-V, --version show program's version number and exitIf no filename specified, use STDIN instead.
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前缀字典。如果你想手工初始 jieba,也可以手动初始化。
import jiebajieba.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: /fxsjy/jieba/blob/master/test/test_change_dictpath.py
其他词典
占用内存较小的词典文件
/fxsjy/jieba/raw/master/extra_dict/dict.txt.small
支持繁体分词更好的词典文件
/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')
其他语言实现
结巴分词 Java 版本
作者:piaolingxue
地址:/huaban/jieba-analysis
结巴分词 C++ 版本
作者:yanyiwu
地址:/yanyiwu/cppjieba
结巴分词 Node.js 版本
作者:yanyiwu
地址:/yanyiwu/nodejieba
结巴分词 Erlang 版本
作者:falood
地址:/falood/exjieba
结巴分词 R 版本
作者:qinwf
地址:/qinwf/jiebaR
结巴分词 iOS 版本
作者:yanyiwu
地址:/yanyiwu/iosjieba
系统集成
Solr: /sing1ee/jieba-solr分词速度
1.5 MB / Second in Full Mode400 KB / Second in Default Mode测试环境: 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?
详见: /fxsjy/jieba/issues/7
2. “台中”总是被切成“台 中”?(以及类似情况)
P(台中) < P(台)×P(中),“台中”词频不够导致其成词概率较低
解决方法:强制调高词频
jieba.add_word('台中')
或者jieba.suggest_freq('台中', True)
3. “今天天气 不错”应该被切成“今天 天气 不错”?(以及类似情况)
解决方法:强制调低词频
jieba.suggest_freq(('今天', '天气'), True)
或者直接删除该词jieba.del_word('今天天气')
4. 切出了词典中没有的词语,效果不理想?
解决方法:关闭新词发现
jieba.cut('丰田太省了', HMM=False)
jieba.cut('我们中出了一个叛徒', HMM=False)
更多问题请点击:/fxsjy/jieba/issues?sort=updated&state=closed
修订历史
/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:
Accurate Mode attempts to cut the sentence into the most accurate segmentations, which is suitable for text analysis.Full Mode gets all the possible words from the sentence. Fast but not accurate.Search Engine Mode, based on the Accurate Mode, attempts to cut long words into several short words, which can raise the recall rate. Suitable for search engines.
Supports Traditional Chinese
Supports customized dictionariesMIT License
Online demo
http://jiebademo.ap01.aws.af.cm/
(Powered by Appfog)
Usage
Fully automatic installation:easy_install jieba
orpip install jieba
Semi-automatic installation: Download /pypi/jieba/ , runpython setup.py install
after extracting.Manual installation: place thejieba
directory in the current directory or pythonsite-packages
directory.import jieba
.Algorithm
Based on a prefix dictionary structure to achieve efficient word graph scanning. Build a directed acyclic graph (DAG) for all possible word combinations.Use dynamic programming to find the most probable combination based on the word frequency.For unknown words, a HMM-based model is used with the Viterbi algorithm.Main Functions
1) : Cut
Thejieba.cut
function accepts three input parameters: the first parameter is the string to be cut; the second parameter iscut_all
, controlling the cut mode; the third parameter is to control whether to use the Hidden Markov Model.jieba.cut_for_search
accepts two parameter: the string to be cut; whether to use the Hidden Markov Model. This will cut the sentence into short words suitable for search engines.The input string can be an unicode/str object, or a str/bytes object which is encoded in UTF-8 or GBK. Note that using GBK encoding is not recommended because it may be unexpectly decoded as UTF-8.jieba.cut
andjieba.cut_for_search
returns an generator, from which you can use afor
loop to get the segmentation result (in unicode), orlist(jieba.cut( ... ))
to create a list.Code example: segmentation
#encoding=utf-8import jiebaseg_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
2) : Add a custom dictionary
Load dictionary
Developers can specify their own custom dictionary to be included in the jieba default dictionary. Jieba is able to identify new words, but adding your own new words can ensure a higher accuracy.Usage:jieba.load_userdict(file_name) # file_name is the path of the custom dictionary
The dictionary format is the same as that ofanalyse/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 spaceExample:
云计算 5李小福 2创新办 3[Before]: 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /[After]:李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
Modify dictionary
Useadd_word(word, freq=None, tag=None)
anddel_word(word)
to modify the dictionary dynamically in programs.Usesuggest_freq(segment, tune=True)
to adjust the frequency of a single word so that it can (or cannot) be segmented.
Note that HMM may affect the final result.
Example:
>>> print('/'.join(jieba.cut('如果放到post中将出错。', HMM=False)))如果/放到/post/中将/出错/。>>> jieba.suggest_freq(('中', '将'), True)494>>> print('/'.join(jieba.cut('如果放到post中将出错。', HMM=False)))如果/放到/post/中/将/出错/。>>> print('/'.join(jieba.cut('「台中」正确应该不会被切开', HMM=False)))「/台/中/」/正确/应该/不会/被/切开>>> jieba.suggest_freq('台中', True)69>>> print('/'.join(jieba.cut('「台中」正确应该不会被切开', HMM=False)))「/台中/」/正确/应该/不会/被/切开
3) : Keyword Extraction
jieba.analyse.extract_tags(sentence,topK,withWeight) # needs to first import jieba.analyse
sentence
: the text to be extractedtopK
: return how many keywords with the highest TF/IDF weights. The default value is 20withWeight
: whether return TF/IDF weights with the keywords. The default value is FalseExample (keyword extraction)
/fxsjy/jieba/blob/master/test/extract_tags.py
Developers can specify their own custom IDF corpus in jieba keyword extraction
Usage:jieba.analyse.set_idf_path(file_name) # file_name is the path for the custom corpus
Custom Corpus Sample:/fxsjy/jieba/blob/master/extra_dict/idf.txt.bigSample Code:/fxsjy/jieba/blob/master/test/extract_tags_idfpath.py
Developers can specify their own custom stop words corpus in jieba keyword extraction
Usage:jieba.analyse.set_stop_words(file_name) # file_name is the path for the custom corpus
Custom Corpus Sample:/fxsjy/jieba/blob/master/extra_dict/stop_words.txtSample Code:/fxsjy/jieba/blob/master/test/extract_tags_stop_words.py
There’s also a TextRank implementation available.
Use:jieba.analyse.textrank(raw_text)
.
4) : Part of Speech Tagging
Tags the POS of each word after segmentation, using labels compatible with ictclas.Example:>>> import jieba.posseg as pseg>>> words = pseg.cut("我爱北京天安门")>>> for w in words:... print('%s %s' % (w.word, w.flag))...我 r爱 v北京 ns天安门 ns
5) : Parallel Processing
Principle: Split target text by line, assign the lines into multiple Python processes, and then merge the results, which is considerably faster.Based on the multiprocessing module of Python.Usage:
jieba.enable_parallel(4)
# Enable parallel processing. The parameter is the number of processes.jieba.disable_parallel()
# Disable parallel processing.
Example:
/fxsjy/jieba/blob/master/test/parallel/test_file.py
Result: On a four-core 3.4GHz Linux machine, do accurate word segmentation on Complete Works of Jin Yong, and the speed reaches 1MB/s, which is 3.3 times faster than the single-process version.
6) : Tokenize: return words with position
The input must be unicodeDefault moderesult = jieba.tokenize(u'永和服装饰品有限公司')for tk in result:print("word %s\t\t start: %d \t\t end:%d" % (tk[0],tk[1],tk[2]))
word 永和start: 0end:2word 服装start: 2end:4word 饰品start: 4end:6word 有限公司 start: 6end:10
Search mode
result = jieba.tokenize(u'永和服装饰品有限公司',mode='search')for tk in result:print("word %s\t\t start: %d \t\t end:%d" % (tk[0],tk[1],tk[2]))
word 永和start: 0end:2word 服装start: 2end:4word 饰品start: 4end:6word 有限start: 6end:8word 公司start: 8end:10word 有限公司 start: 6end:10
7) : ChineseAnalyzer for Whoosh
from jieba.analyse import ChineseAnalyzer
Example: /fxsjy/jieba/blob/master/test/test_whoosh.py8) : Command Line Interface
$> python -m jieba --helpusage: python -m jieba [options] filenameJieba command line interface.positional arguments:filename input fileoptional arguments:-h, --help show this help message and exit-d [DELIM], --delimiter [DELIM]use DELIM instead of ' / ' for word delimiter; or aspace if it is used without DELIM-D DICT, --dict DICT use DICT as dictionary-u USER_DICT, --user-dict USER_DICTuse USER_DICT together with the default dictionary orDICT (if specified)-a, --cut-all full pattern cutting-n, --no-hmmdon't use the Hidden Markov Model-q, --quiet don't print loading messages to stderr-V, --version show program's version number and exitIf no filename specified, use STDIN instead.
Initialization
By default, Jieba don’t build the prefix dictionary unless it’s necessary. This takes 1-3 seconds, after which it is not initialized again. If you want to initialize Jieba manually, you can call:
import jiebajieba.initialize() # (optional)
You can also specify the dictionary (not supported before version 0.28) :
jieba.set_dictionary('data/dict.txt.big')
Using Other Dictionaries
It is possible to use your own dictionary with Jieba, and there are also two dictionaries ready for download:
A smaller dictionary for a smaller memory footprint:
/fxsjy/jieba/raw/master/extra_dict/dict.txt.small
There is also a bigger dictionary that has better support for traditional Chinese (繁體):
/fxsjy/jieba/raw/master/extra_dict/dict.txt.big
By default, an in-between dictionary is used, calleddict.txt
and included in the distribution.
In either case, download the file you want, and then calljieba.set_dictionary('data/dict.txt.big')
or just replace the existingdict.txt
.