Chinese text segmentation with R. R语言中文分词

github地址：https://github.com/cran/jiebaR

jiebaR

Win :

　DOI
:



Linux :

　
Mac :

　

"结巴"中文分词的R语言版本，支持最大概率法（Maximum Probability），隐式马尔科夫模型（Hidden Markov Model），索引模型（QuerySegment），混合模型（MixSegment），共四种分词模式，同时有词性标注，关键词提取，文本Simhash相似度比较等功能。项目使用了Rcpp和CppJieba进行开发。

你还可以试试支持中文编程的 Julia 分词 Jieba.jl。

特性

支持 Windows，Linux，Mac 操作系统。
通过 Rcpp 实现同时加载多个分词系统,可以分别使用不同的分词模式和词库。
支持多种分词模式、中文姓名识别、关键词提取、词性标注以及文本Simhash相似度比较等功能。
支持加载自定义用户词库，设置词频、词性。
同时支持简体中文、繁体中文分词。
支持自动判断编码模式。
比原"结巴"中文分词速度快，是其他R分词包的5-20倍。
安装简单，无需复杂设置。
可以通过Rpy2，jvmr等被其他语言调用。
基于MIT协议。

CRAN
版更新 v0.4

重构C++部分代码，移除Rcpp Modules，加快包加载速度和函数调用速度。
优化筛选标点符号的正则表达式，现可识别生僻汉字。
遵守 CRAN 的要求，分离大文件到 jiebaRD 包

安装

通过CRAN安装:

install.packages("jiebaR")
library("jiebaR")

同时还可以通过Github安装开发版,建议使用 gcc >= 4.6 编译包：

library(devtools)
install_github("qinwf/jiebaRD")
install_github("qinwf/jiebaR")
library("jiebaR")

使用示例

分词

jiebaR提供了四种分词模式，可以通过

worker()

来初始化分词引擎，使用

segment()

进行分词。

##  接受默认参数，建立分词引擎
mixseg = worker()

##  相当于：
##       worker( type = "mix", dict = "inst/dict/jieba.dict.utf8",
##               hmm  = "inst/dict/hmm_model.utf8",  ### HMM模型数据
##               user = "inst/dict/user.dict.utf8") ### 用户自定义词库

mixseg <= "江州市长江大桥参加了长江大桥的通车仪式"  ### <= 分词运算符

## 相当于 segment( "江州市长江大桥参加了长江大桥的通车仪式" , mixseg )

[1] "江州"     "市长"     "江大桥"   "参加"     "了"       "长江大桥"
[7] "的"       "通车"     "仪式"

支持对文件进行分词：

mixseg <= "./temp.dat"  ### 自动判断输入文件编码模式，默认文件输出在同目录下。

## segment( "./temp.dat" , mixseg )

在加载分词引擎时，可以自定义词库路径，同时可以启动不同的引擎：

最大概率法（MPSegment），负责根据Trie树构建有向无环图和进行动态规划算法，是分词算法的核心。

隐式马尔科夫模型（HMMSegment）是根据基于人民日报等语料库构建的HMM模型来进行分词，主要算法思路是根据(B,E,M,S)四个状态来代表每个字的隐藏状态。 HMM模型由dict/hmm_model.utf8提供。分词算法即viterbi算法。

混合模型（MixSegment）是四个分词引擎里面分词效果较好的类，结它合使用最大概率法和隐式马尔科夫模型。

索引模型（QuerySegment）先使用混合模型进行切词，再对于切出来的较长的词，枚举句子中所有可能成词的情况，找出词库里存在。

mixseg2 = worker(type  = "mix", dict = "dict/jieba.dict.utf8",
hmm   = "dict/hmm_model.utf8",
user  = "dict/test.dict.utf8",
detect=T,      symbol = F,
lines = 1e+05, output = NULL
)
mixseg2   ### 输出worker的设置

Worker Type:  Mix Segment

Detect Encoding :  TRUE
Default Encoding:  UTF-8
Keep Symbols    :  FALSE
Output Path     :
Write File      :  TRUE
Max Read Lines  :  1e+05

Fixed Model Components:

$dict
[1] "dict/jieba.dict.utf8"

$hmm
[1] "dict/hmm_model.utf8"

$user
[1] "dict/test.dict.utf8"

$detect $encoding $symbol $output $write $lines can be reset.

可以通过R语言常用的

$

符号重设一些

worker

的参数设置，如

WorkerName$symbol
= T

，在输出中保留标点符号。一些参数在初始化的时候已经确定，无法修改， 可以通过

WorkerName$PrivateVarible

来获得这些信息。

mixseg$encoding

mixseg$detect = F

可以自定义用户词库，推荐使用深蓝词库转换构建分词词库，它可以快速地将搜狗细胞词库等输入法词库转换为jiebaR的词库格式。

show_dictpath()     ### 显示词典路径
edit_dict("user")   ### 编辑用户词典
?edit_dict()        ### 打开帮助系统

系统词典共有三列，第一列为词项，第二列为词频，第三列为词性标记。

用户词典有两列，第一列为词项，第二列为词性标记。用户词库默认词频为系统词库中的最大词频，如需自定义词频率，可将新词添加入系统词库中。

词典中的词性标记采用ictclas的标记方法。

快速模式

无需使用

worker()

，使用默认参数启动引擎，并立即进行分词：

library(jiebaR)

qseg <= "江州市长江大桥参加了长江大桥的通车仪式"

[1] "江州"     "市长"     "江大桥"   "参加"     "了"       "长江大桥" "的"
[8] "通车"     "仪式"

qseg

~ quick segmentation，使用默认分词模式，自动建立分词引擎，类似于

ggplot2

包里面的

qplot

函数。

### 第一次运行时，启动默认引擎 quick_worker，第二次运行，不再启动引擎。
qseg <= "这是测试文本。"

[1] "这是" "测试" "文本"

### 效果相同
quick_worker <=  "这是测试文本。"

qseg

Worker Type:  Mix Segment

Detect Encoding :  TRUE
Default Encoding:  UTF-8
Keep Symbols    :  FALSE
Output Path     :  NULL
.......

可以通过

qseg$

重设模型参数，重设模型参数将会修改以后每次默认启动的默认参数，如果只是希望单次修改模型参数，可以使用非快速模式的修改方式

quick_worker$

。

qseg$type = "mp" ### 重设模型参数的同时，重新启动引擎。

qseg$type        ### 下次重新启动包是将使用现在的参数，构建模型。

quick_worker$detect = T ### 临时修改，对下次重新启动包时，没有影响。

get_qsegmodel()         ### 获得当前快速模式的默认参数

词性标注

可以使用

<=.tagger

或者

tag

来进行分词和词性标注，词性标注使用混合模型模型分词，标注采用和
ictclas 兼容的标记法。

words = "我爱北京天安门"
tagger = worker("tag")
tagger <= words

r        v       ns       ns
"我"     "爱"   "北京" "天安门"

关键词提取

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，使用方法与分词类似。

topn

参数为关键词的个数。

keys = worker("keywords", topn = 1)
keys <= "我爱北京天安门"
keys <= "一个文件路径.txt"

8.9954
"天安门"

Simhash
与海明距离

对中文文档计算出对应的simhash值。simhash是谷歌用来进行文本去重的算法，现在广泛应用在文本处理中。Simhash引擎先进行分词和关键词提取，后计算Simhash值和海明距离。

words = "hello world!"
simhasher = worker("simhash",topn=2)
simhasher <= "江州市长江大桥参加了长江大桥的通车仪式"

$simhash
[1] "12882166450308878002"

$keyword
22.3853    8.69667
"长江大桥"     "江州"

distance("江州市长江大桥参加了长江大桥的通车仪式" , "hello world!", simhasher)

$distance
[1] "23"

$lhs
22.3853    8.69667
"长江大桥"     "江州"

$rhs
11.7392 11.7392
"hello" "world"

计划支持

支持 Windows , Linux , Mac 操作系统并行分词。
简单的自然语言统计分析功能。

jiebaR

This is a package for Chinese text segmentation, keyword extraction and speech tagging.

jiebaR

supports four types of segmentation
modes: Maximum Probability, Hidden Markov Model, Query Segment and Mix Segment.

Features

Support Windows, Linux,and Mac.
Using Rcpp to load different segmentation worker at the same time.
Support Chinese text segmentation, keyword extraction, speech tagging and simhash computation.
Custom dictionary path.
Support simplified Chinese and traditional Chinese.
New words identification.
Auto encoding detection.
Fast text segmentation.
Easy installation.
MIT license.

Installation

Install the latest development version from GitHub:

devtools::install_github("qinwf/jiebaR")

Install from CRAN:

install.packages("jiebaR")

Example

Text
Segmentation

There are four segmentation models. You can use

worker()

to initialize a worker, and then use

<=

or

segment()

to
do the segmentation.

library(jiebaR)

##  Using default argument to initialize worker.
cutter = worker()

##       jiebar( type = "mix", dict = "dictpath/jieba.dict.utf8",
##               hmm  = "dictpath/hmm_model.utf8",  ### HMM model data
##               user = "dictpath/user.dict.utf8") ### user dictionary

###  Note: Can not display Chinese character here.

cutter <= "This is a good day!"

## OR segment( "This is a good day!" , cutter )

[1] "This" "is"   "a"    "good" "day"

You can pipe a file path to cut file.

cutter <= "./temp.dat"  ### Auto encoding detection.

## OR segment( "./temp.dat" , cutter )

The package uses initialized engines for word segmentation. You can initialize multiple engines simultaneously.

cutter2 = worker(type  = "mix", dict = "dict/jieba.dict.utf8",
hmm   = "dict/hmm_model.utf8",
user  = "dict/test.dict.utf8",
detect=T,      symbol = F,
lines = 1e+05, output = NULL
)
cutter2   ### Print information of worker

Worker Type:  Mix Segment

Detect Encoding :  TRUE
Default Encoding:  UTF-8
Keep Symbols    :  FALSE
Output Path     :
Write File      :  TRUE
Max Read Lines  :  1e+05

Fixed Model Components:

$dict
[1] "dict/jieba.dict.utf8"

$hmm
[1] "dict/hmm_model.utf8"

$user
[1] "dict/test.dict.utf8"

$detect $encoding $symbol $output $write $lines can be reset.

The model public settings can be modified and got using

$

, such as

WorkerName$symbol
= T

. Some private settings are fixed when the engine is initialized, and you can get them by

WorkerName$PrivateVarible

.

cutter$encoding

cutter$detect = F

Users can specify their own custom dictionary to be included in the jiebaR default dictionary. jiebaR is able to identify new words, but adding your own new words can ensure a higher accuracy.imewlconverter is
a good tools for dictionary construction.

show_dictpath()  ### Show path
edit_dict()      ### Edit user dictionary
?edit_dict()     ### For more information

There are three column in the system dictionary. The first column is the word, and the second column is the frequency of word. The third column is speech tag using labels compatible with ictclas.

There are two column in the user dictionary. The first column is the word, and the second column is speech tag using labels compatible with ictclas. Frequency of every word in the user dictionary will be the maximum number of the system dictionary. If you want
to provide the frequency for a new word, you can put it in the system dictionary.

Speech
Tagging

Speech Tagging function

<=.tagger

or

tag

uses
speech tagging worker to cut word and tags each word after segmentation, using labels compatible with ictclas.

dict

hmm

and

user

should
be provided when initializing

jiebaR

worker.

words = "hello world"
tagger = worker("tag")
tagger <= words

x       x
"hello" "world"

Keyword
Extraction

Keyword Extraction worker use MixSegment model to cut word and use TF-IDF algorithm to find the keywords.

dict

,

hmm

,

idf

,

stop_word

and

topn

should
be provided when initializing

jiebaR

worker.

keys = worker("keywords", topn = 1)
keys <= "words of fun"

11.7392
"fun"

Simhash
Distance

Simhash worker can do keyword extraction and find the keywords from two inputs, and then computes Hamming distance between them.

words = "hello world"
simhasher = worker("simhash",topn=1)
simhasher <= words

$simhash
[1] "3804341492420753273"

$keyword
11.7392
"hello"

distance("hello world" , "hello world!" , simhasher)

$distance
[1] "0"

$lhs
11.7392
"hello"

$rhs
11.7392
"hello"

Future
Development

Support parallel programming on Windows , Linux , Mac.
Simple Natural Language Processing features.

More
Information and Issues

https://github.com/qinwf/jiebaR

https://github.com/aszxqw/cppjieba