zry acf5664a4a 将内容添加到空仓库 | преди 8 години | |
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analyse | преди 8 години | |
finalseg | преди 8 години | |
posseg | преди 8 години | |
Changelog | преди 8 години | |
LICENSE | преди 8 години | |
README.md | преди 8 години | |
__init__.py | преди 8 години | |
__init__.pyc | преди 8 години | |
dict.txt | преди 8 години |
"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.
支持三种分词模式:
支持繁体分词
支持自定义词典
http://jiebademo.ap01.aws.af.cm/
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网站代码:https://github.com/fxsjy/jiebademo
easy_install jieba
或者 pip install jieba
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
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
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:
自定义词典:https://github.com/fxsjy/jieba/blob/master/test/userdict.txt
用法示例:https://github.com/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 /
"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
代码示例 (关键词提取)
https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
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
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。
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: 0 end:2
word 服装 start: 2 end:4
word 饰品 start: 4 end:6
word 有限公司 start: 6 end: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: 0 end:2
word 服装 start: 2 end:4
word 饰品 start: 4 end:6
word 有限 start: 6 end:8
word 公司 start: 8 end:10
word 有限公司 start: 6 end:10
from jieba.analyse import ChineseAnalyzer
占用内存较小的词典文件 https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.small
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')
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
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
https://github.com/fxsjy/jieba/blob/master/Changelog
"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.
easy_install jieba
or pip install jieba
python setup.py install
jieba
directory in the current directory or python site-packages directory.import jieba
to import, which will first build the Trie tree only on first import (takes a few seconds).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#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
jieba.load_userdict(file_name) # file_name is a custom dictionary path
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 spaceExample:
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 /
jieba.analyse.extract_tags(sentence,topK) # needs to first import jieba.analyse
setence
: the text to be extractedtopK
: To return several TF / IDF weights for the biggest keywords, the default value is 20Code sample (keyword extraction)
https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
It is possible to supply Jieba with your own custom dictionary, and there are also two dictionaries readily available for download:
You can employ a smaller dictionary for a smaller memory footprint: https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.small
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
.
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')
http://jiebademo.ap01.aws.af.cm/
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