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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!
- Python 2.7
- Python 3.x
- checked in 3.5, 3.6, 3.7
- 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)
Mecab is open source tokenizer system for various language(if you have dictionary for it)
See english documentation for detail
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++ 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 is tokenizer tool developped by Graham Neubig.
Kytea has a different algorithm from one of Mecab or Juman.
mecab-neologd dictionary auto-install
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.
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
- 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
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
[sudo] python setup.py install
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.
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())
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())
Mecab, Juman, Kytea have different system of Part-of-Speech(POS).
You can check tables of Part-of-Speech(POS) here
natto-py is sophisticated package for tokenization. It supports following features
- easy interface for tokenization
- importing additional dictionary
- partial parsing mode
You could build an environment which has dependencies to test this package.
Simply, you build docker image and run docker container.
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
These commands checks from procedures of package install until test of package.
$ docker-compose build $ docker-compose up
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