{ "info": { "author": "Yukino Ikegami", "author_email": "yknikgm@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Natural Language :: Japanese", "Programming Language :: Cython", "Programming Language :: Python", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Text Processing :: Linguistic" ], "description": "neologdn\n===========\n\n|travis| |pyversion| |version| |landscape| |license|\n\nneologdn is a Japanese text normalizer for `mecab-neologd `_.\n\nThe normalization is based on the neologd's rules:\nhttps://github.com/neologd/mecab-ipadic-neologd/wiki/Regexp.ja\n\n\nContributions are welcome!\n\nNOTE: Installing this module requires C++11 compiler.\n\nInstallation\n------------\n\n::\n\n $ pip install neologdn\n\nUsage\n-----\n\n.. code:: python\n\n import neologdn\n neologdn.normalize(\"\uff8a\uff9d\uff76\uff78\uff76\uff85\")\n # => '\u30cf\u30f3\u30ab\u30af\u30ab\u30ca'\n neologdn.normalize(\"\u5168\u89d2\u8a18\u53f7\uff01\uff1f\uff20\uff03\")\n # => '\u5168\u89d2\u8a18\u53f7!?@#'\n neologdn.normalize(\"\u5168\u89d2\u8a18\u53f7\u4f8b\u5916\u300c\u30fb\u300d\")\n # => '\u5168\u89d2\u8a18\u53f7\u4f8b\u5916\u300c\u30fb\u300d'\n neologdn.normalize(\"\u9577\u97f3\u77ed\u7e2e\u30a6\u30a7\u30fc\u30fc\u30fc\u30fc\u30a4\")\n # => '\u9577\u97f3\u77ed\u7e2e\u30a6\u30a7\u30fc\u30a4'\n neologdn.normalize(\"\u30c1\u30eb\u30c0\u524a\u9664\u30a6\u30a7~\u223c\u223e\u301c\u3030\uff5e\u30a4\")\n # => '\u30c1\u30eb\u30c0\u524a\u9664\u30a6\u30a7\u30a4'\n neologdn.normalize(\"\u3044\u308d\u3093\u306a\u30cf\u30a4\u30d5\u30f3\u02d7\u058a\u2010\u2011\u2012\u2013\u2043\u207b\u208b\u2212\")\n # => '\u3044\u308d\u3093\u306a\u30cf\u30a4\u30d5\u30f3-'\n neologdn.normalize(\"\u3000\u3000\u3000\uff30\uff32\uff2d\uff2c\u3000\u3000\u526f\u3000\u8aad\u3000\u672c\u3000\u3000\u3000\")\n # => 'PRML\u526f\u8aad\u672c'\n neologdn.normalize(\" Natural Language Processing \")\n # => 'Natural Language Processing'\n neologdn.normalize(\"\u304b\u308f\u3044\u3044\u3044\u3044\u3044\u3044\u3044\u3044\u3044\", repeat=6)\n # => '\u304b\u308f\u3044\u3044\u3044\u3044\u3044\u3044'\n neologdn.normalize(\"\u7121\u99c4\u7121\u99c4\u7121\u99c4\u7121\u99c4\u30a1\", repeat=1)\n # => '\u7121\u99c4\u30a1'\n\n\nBenchmark\n----------\n\n.. code:: python\n\n # Sample code from\n # https://github.com/neologd/mecab-ipadic-neologd/wiki/Regexp.ja#python-written-by-hideaki-t--overlast\n import normalize_neologd\n\n %timeit normalize(normalize_neologd.normalize_neologd)\n # => 1 loop, best of 3: 18.3 s per loop\n\n\n import neologdn\n %timeit normalize(neologdn.normalize)\n # => 1 loop, best of 3: 9.05 s per loop\n\n\nneologdn is about x2 faster than sample code.\n\ndetails are described as the below notebook:\nhttps://github.com/ikegami-yukino/neologdn/blob/master/benchmark/benchmark.ipynb\n\n\nLicense\n-------\n\nApache Software License.\n\n\n.. |travis| image:: https://travis-ci.org/ikegami-yukino/neologdn.svg?branch=master\n :target: https://travis-ci.org/ikegami-yukino/neologdn\n :alt: travis-ci.org\n\n.. |version| image:: https://img.shields.io/pypi/v/neologdn.svg\n :target: http://pypi.python.org/pypi/neologdn/\n :alt: latest version\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/neologdn.svg\n\n.. |landscape| image:: https://landscape.io/github/ikegami-yukino/neologdn/master/landscape.svg?style=flat\n :target: https://landscape.io/github/ikegami-yukino/neologdn/master\n :alt: Code Health\n\n.. |license| image:: https://img.shields.io/pypi/l/neologdn.svg\n :target: http://pypi.python.org/pypi/neologdn/\n :alt: license\n\n\n\nCHANGES\n========\n\n0.4 (2018-12-06)\n----------------------------\n\n- Add shorten_repeat function, which shortening contiguous substring. 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