What’s this?
This is simple python-wrapper for Japanese Tokenizers(A.K.A Tokenizer)
This project aims to call tokenizers and split a sentence into tokens as easy as possible.
And, this project supports various Tokenization tools common interface. Thus, it’s easy to compare output from various tokenizers.
This project is available also in Github.
If you find any bugs, please report them to github issues. Or any pull requests are welcomed!
Requirements
Python 2.7
Python 3.x
checked in 3.5, 3.6, 3.7
Features
simple/common interface among various tokenizers
simple/common interface for filtering with stopwords or Part-of-Speech condition
simple interface to add user-dictionary(mecab only)
Supported Tokenizers
Mecab
Mecab is open source tokenizer system for various language(if you have dictionary for it)
See english documentation for detail
Juman
Juman is a tokenizer system developed by Kurohashi laboratory, Kyoto University, Japan.
Juman is strong for ambiguous writing style in Japanese, and is strong for new-comming words thanks to Web based huge dictionary.
And, Juman tells you semantic meaning of words.
Juman++
Juman++ is a tokenizer system developed by Kurohashi laboratory, Kyoto University, Japan.
Juman++ is succeeding system of Juman. It adopts RNN model for tokenization.
Juman++ is strong for ambigious writing style in Japanese, and is strong for new-comming words thanks to Web based huge dictionary.
And, Juman tells you semantic meaning of words.
Note: New Juman++ dev-version(later than 2.x) is available at Github
Kytea
Kytea is tokenizer tool developped by Graham Neubig.
Kytea has a different algorithm from one of Mecab or Juman.
Setting up
Tokenizers auto-install
make install
mecab-neologd dictionary auto-install
make install_neologd
Tokenizers manual-install
MeCab
See here to install MeCab system.
Mecab Neologd dictionary
Mecab-neologd dictionary is a dictionary-extension based on ipadic-dictionary, which is basic dictionary of Mecab.
With, Mecab-neologd dictionary, you’re able to parse new-coming words make one token.
Here, new-coming words is such like, movie actor name or company name…..
See here and install mecab-neologd dictionary.
Juman
wget -O juman7.0.1.tar.bz2 "http://nlp.ist.i.kyoto-u.ac.jp/DLcounter/lime.cgi?down=http://nlp.ist.i.kyoto-u.ac.jp/nl-resource/juman/juman-7.01.tar.bz2&name=juman-7.01.tar.bz2" bzip2 -dc juman7.0.1.tar.bz2 | tar xvf - cd juman-7.01 ./configure make [sudo] make install
Juman++
GCC version must be >= 5
wget http://lotus.kuee.kyoto-u.ac.jp/nl-resource/jumanpp/jumanpp-1.02.tar.xz tar xJvf jumanpp-1.02.tar.xz cd jumanpp-1.02/ ./configure make [sudo] make install
Kytea
Install Kytea system
wget http://www.phontron.com/kytea/download/kytea-0.4.7.tar.gz tar -xvf kytea-0.4.7.tar cd kytea-0.4.7 ./configure make make install
Kytea has python wrapper thanks to michiaki ariga. Install Kytea-python wrapper
pip install kytea
install
[sudo] python setup.py install
Note
During install, you see warning message when it fails to install pyknp or kytea.
if you see these messages, try to re-install these packages manually.
Usage
Tokenization Example(For python3.x. To see exmaple code for Python2.x, plaese see here)
import JapaneseTokenizer input_sentence = '10日放送の「中居正広のミになる図書館」（テレビ朝日系）で、SMAPの中居正広が、篠原信一の過去の勘違いを明かす一幕があった。' # ipadic is well-maintained dictionary # mecab_wrapper = JapaneseTokenizer.MecabWrapper(dictType='ipadic') print(mecab_wrapper.tokenize(input_sentence).convert_list_object()) # neologd is automatically-generated dictionary from huge web-corpus # mecab_neologd_wrapper = JapaneseTokenizer.MecabWrapper(dictType='neologd') print(mecab_neologd_wrapper.tokenize(input_sentence).convert_list_object())
Filtering example
import JapaneseTokenizer
# with word filtering by stopword & part-of-speech condition #
print(mecab_wrapper.tokenize(input_sentence).filter(stopwords=['テレビ朝日'], pos_condition=[('名詞', '固有名詞')]).convert_list_object())
Part-of-speech structure
Mecab, Juman, Kytea have different system of Part-of-Speech(POS).
You can check tables of Part-of-Speech(POS) here
Similar Package
natto-py
natto-py is sophisticated package for tokenization. It supports following features
easy interface for tokenization
importing additional dictionary
partial parsing mode
LICENSE
MIT license
For developers
You could build an environment which has dependencies to test this package.
Simply, you build docker image and run docker container.
Dev environment
Develop environment is defined with test/docker-compose-dev.yml.
With the docker-compose.yml file, you could call python2.7 or python3.7
If you’re using Pycharm Professional edition, you could set docker-compose.yml as remote interpreter.
To call python2.7, set /opt/conda/envs/p27/bin/python2.7
To call python3.7, set /opt/conda/envs/p37/bin/python3.7
Test environment
These commands checks from procedures of package install until test of package.
$ docker-compose build
$ docker-compose up1 maintainer
Release files for JapaneseTokenizer 1.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| JapaneseTokenizer-1.6.tar.gz | 29.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| JapaneseTokenizer-1.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 74.0 kB
Release files / JapaneseTokenizer-1.6.tar.gz
| Download URL | JapaneseTokenizer-1.6.tar.gz |
|---|---|
| Size | 29.5 kB |
| Tags | Source |
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Release files / JapaneseTokenizer-1.6-py3-none-any.whl
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| Tags | Python 3 |
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