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        "author": "Kensuke Mitsuzawa",
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        "description": "|MIT License|\\ |Build Status|\n\nWhat\u2019s this?\n============\n\nThis is simple python-wrapper for Japanese Tokenizers(A.K.A Tokenizer)\n\nThis project aims to call tokenizers and split a sentence into tokens as\neasy as possible.\n\nAnd, this project supports various Tokenization tools common interface.\nThus, it\u2019s easy to compare output from various tokenizers.\n\nThis project is available also in\n`Github <https://github.com/Kensuke-Mitsuzawa/JapaneseTokenizers>`__.\n\nIf you find any bugs, please report them to github issues. Or any pull\nrequests are welcomed!\n\nRequirements\n============\n\n-  Python 2.7\n-  Python 3.x\n\n   -  checked in 3.5, 3.6, 3.7\n\nFeatures\n========\n\n-  simple/common interface among various tokenizers\n-  simple/common interface for filtering with stopwords or\n   Part-of-Speech condition\n-  simple interface to add user-dictionary(mecab only)\n\nSupported Tokenizers\n--------------------\n\nMecab\n~~~~~\n\n`Mecab <http://mecab.googlecode.com/svn/trunk/mecab/doc/index.html?sess=3f6a4f9896295ef2480fa2482de521f6>`__\nis open source tokenizer system for various language(if you have\ndictionary for it)\n\nSee `english\ndocumentation <https://github.com/jordwest/mecab-docs-en>`__ for detail\n\nJuman\n~~~~~\n\n`Juman <http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?JUMAN>`__ is a\ntokenizer system developed by Kurohashi laboratory, Kyoto University,\nJapan.\n\nJuman is strong for ambiguous writing style in Japanese, and is strong\nfor new-comming words thanks to Web based huge dictionary.\n\nAnd, Juman tells you semantic meaning of words.\n\n.. _juman-1:\n\nJuman++\n~~~~~~~\n\n`Juman++ <http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?JUMAN++>`__ is a\ntokenizer system developed by Kurohashi laboratory, Kyoto University,\nJapan.\n\nJuman++ is succeeding system of Juman. It adopts RNN model for\ntokenization.\n\nJuman++ is strong for ambigious writing style in Japanese, and is strong\nfor new-comming words thanks to Web based huge dictionary.\n\nAnd, Juman tells you semantic meaning of words.\n\nNote: New Juman++ dev-version(later than 2.x) is available at\n`Github <https://github.com/ku-nlp/jumanpp>`__\n\nKytea\n~~~~~\n\n`Kytea <http://www.phontron.com/kytea/>`__ is tokenizer tool developped\nby Graham Neubig.\n\nKytea has a different algorithm from one of Mecab or Juman.\n\nSetting up\n==========\n\nTokenizers auto-install\n-----------------------\n\n::\n\n   make install\n\nmecab-neologd dictionary auto-install\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n   make install_neologd\n\nTokenizers manual-install\n-------------------------\n\n.. _mecab-1:\n\nMeCab\n~~~~~\n\nSee `here <https://github.com/jordwest/mecab-docs-en>`__ to install\nMeCab system.\n\nMecab Neologd dictionary\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nMecab-neologd dictionary is a dictionary-extension based on\nipadic-dictionary, which is basic dictionary of Mecab.\n\nWith, Mecab-neologd dictionary, you\u2019re able to parse new-coming words\nmake one token.\n\nHere, new-coming words is such like, movie actor name or company name\u2026..\n\nSee `here <https://github.com/neologd/mecab-ipadic-neologd>`__ and\ninstall mecab-neologd dictionary.\n\n.. _juman-2:\n\nJuman\n~~~~~\n\n::\n\n   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\"\n   bzip2 -dc juman7.0.1.tar.bz2  | tar xvf -\n   cd juman-7.01\n   ./configure\n   make   \n   [sudo] make install\n\n.. _juman-3:\n\nJuman++\n-------\n\n-  GCC version must be >= 5\n\n::\n\n   wget http://lotus.kuee.kyoto-u.ac.jp/nl-resource/jumanpp/jumanpp-1.02.tar.xz\n   tar xJvf jumanpp-1.02.tar.xz\n   cd jumanpp-1.02/\n   ./configure\n   make\n   [sudo] make install\n\n.. _kytea-1:\n\nKytea\n-----\n\nInstall Kytea system\n\n::\n\n   wget http://www.phontron.com/kytea/download/kytea-0.4.7.tar.gz\n   tar -xvf kytea-0.4.7.tar\n   cd kytea-0.4.7\n   ./configure\n   make\n   make install\n\nKytea has `python wrapper <https://github.com/chezou/Mykytea-python>`__\nthanks to michiaki ariga. Install Kytea-python wrapper\n\n::\n\n   pip install kytea\n\ninstall\n-------\n\n::\n\n   [sudo] python setup.py install\n\nNote\n~~~~\n\nDuring install, you see warning message when it fails to install\n``pyknp`` or ``kytea``.\n\nif you see these messages, try to re-install these packages manually.\n\nUsage\n=====\n\nTokenization Example(For python3.x. To see exmaple code for Python2.x,\nplaese see\n`here <https://github.com/Kensuke-Mitsuzawa/JapaneseTokenizers/blob/master/examples/examples.py>`__)\n\n::\n\n   import JapaneseTokenizer\n   input_sentence = '10\u65e5\u653e\u9001\u306e\u300c\u4e2d\u5c45\u6b63\u5e83\u306e\u30df\u306b\u306a\u308b\u56f3\u66f8\u9928\u300d\uff08\u30c6\u30ec\u30d3\u671d\u65e5\u7cfb\uff09\u3067\u3001SMAP\u306e\u4e2d\u5c45\u6b63\u5e83\u304c\u3001\u7be0\u539f\u4fe1\u4e00\u306e\u904e\u53bb\u306e\u52d8\u9055\u3044\u3092\u660e\u304b\u3059\u4e00\u5e55\u304c\u3042\u3063\u305f\u3002'\n   # ipadic is well-maintained dictionary #\n   mecab_wrapper = JapaneseTokenizer.MecabWrapper(dictType='ipadic')\n   print(mecab_wrapper.tokenize(input_sentence).convert_list_object())\n\n   # neologd is automatically-generated dictionary from huge web-corpus #\n   mecab_neologd_wrapper = JapaneseTokenizer.MecabWrapper(dictType='neologd')\n   print(mecab_neologd_wrapper.tokenize(input_sentence).convert_list_object())\n\nFiltering example\n-----------------\n\n::\n\n   import JapaneseTokenizer\n   # with word filtering by stopword & part-of-speech condition #\n   print(mecab_wrapper.tokenize(input_sentence).filter(stopwords=['\u30c6\u30ec\u30d3\u671d\u65e5'], pos_condition=[('\u540d\u8a5e', '\u56fa\u6709\u540d\u8a5e')]).convert_list_object())\n\nPart-of-speech structure\n------------------------\n\nMecab, Juman, Kytea have different system of Part-of-Speech(POS).\n\nYou can check tables of Part-of-Speech(POS)\n`here <http://www.unixuser.org/~euske/doc/postag/>`__\n\nSimilar Package\n===============\n\nnatto-py\n--------\n\nnatto-py is sophisticated package for tokenization. It supports\nfollowing features\n\n-  easy interface for tokenization\n-  importing additional dictionary\n-  partial parsing mode\n\nLICENSE\n=======\n\nMIT license\n\nFor developers\n==============\n\nYou could build an environment which has dependencies to test this\npackage.\n\nSimply, you build docker image and run docker container.\n\nDev environment\n---------------\n\nDevelop environment is defined with ``test/docker-compose-dev.yml``.\n\nWith the docker-compose.yml file, you could call python2.7 or python3.7\n\nIf you\u2019re using Pycharm Professional edition, you could set\ndocker-compose.yml as remote interpreter.\n\nTo call python2.7, set ``/opt/conda/envs/p27/bin/python2.7``\n\nTo call python3.7, set ``/opt/conda/envs/p37/bin/python3.7``\n\nTest environment\n----------------\n\nThese commands checks from procedures of package install until test of\npackage.\n\n.. code:: bash\n\n   $ docker-compose build\n   $ docker-compose up\n\n.. |MIT License| image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat\n   :target: LICENSE\n.. |Build Status| image:: https://travis-ci.org/Kensuke-Mitsuzawa/JapaneseTokenizers.svg?branch=master\n   :target: https://travis-ci.org/Kensuke-Mitsuzawa/JapaneseTokenizers\n\n\n",
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