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"Programming Language :: Python :: 2.6",
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"description": "Python Word Segmentation\n========================\n\n`zh_segment`_ is an Apache2 licensed module for English word\nsegmentation, written in pure-Python, and based on a trillion-word corpus.\n\nBased on code from the chapter \"`Natural Language Corpus Data`_\" by Peter\nNorvig from the book \"`Beautiful Data`_\" (Segaran and Hammerbacher, 2009).\n\nData files are derived from the `Google Web Trillion Word Corpus`_, as\ndescribed by Thorsten Brants and Alex Franz, and `distributed`_ by the\nLinguistic Data Consortium. This module contains only a subset of that\ndata. The unigram data includes only the most common 333,000 words. Similarly,\nbigram data includes only the most common 250,000 phrases. Every word and\nphrase is lowercased with punctuation removed.\n\n.. _`zh_segment`: https://github.com/wuhaifengdhu/zh_segment/tree/master/docs\n.. _`Natural Language Corpus Data`: http://norvig.com/ngrams/\n.. _`Beautiful Data`: http://oreilly.com/catalog/9780596157111/\n.. _`Google Web Trillion Word Corpus`: http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html\n.. _`distributed`: https://catalog.ldc.upenn.edu/LDC2006T13\n\nFeatures\n--------\n\n- Pure-Python\n- Fully documented\n- 100% Test Coverage\n- Includes unigram and bigram data\n- Command line interface for batch processing\n- Easy to hack (e.g. different scoring, new data, different language)\n- Developed on Python 2.7\n- Tested on CPython 2.6, 2.7, 3.2, 3.3, 3.4 and PyPy 2.5+, PyPy3 2.4+\n\n.. image:: https://github.com/wuhaifengdhu/zh_segment/blob/master/docs/_static/zh_segment.png?raw=true\n :target: https://github.com/wuhaifengdhu/zh_segment\n\nQuickstart\n----------\n\nInstalling zh_segment is simple with\n`pip `_::\n\n $ pip install zh_segment\n\nYou can access documentation in the interpreter with Python's built-in help\nfunction::\n\n >>> import zh_segment\n >>> help(zh_segment)\n\nTutorial\n--------\n\nIn your own Python programs, you'll mostly want to use `segment` to divide a\nphrase into a list of its parts::\n\n >>> from zh_segment import segment\n >>> segment('1077501; 1296599; 5000; 5000; 4975; 36 months; 10.64%; 162.87; B; B2;;10+ years;RENT')\n ['1077501', '1296599', '5000', '5000', '4975', '36', 'months', '10.64%', '162.87', 'B', 'B', '2', '10+', 'years', 'RENT']\n\nzh_segment also provides a command-line interface for batch processing. This\ninterface accepts two arguments: in-file and out-file. Lines from in-file are\niteratively segmented, joined by a space, and written to out-file. Input and\noutput default to stdin and stdout respectively. ::\n\n $ echo thisisatest | python -m zh_segment\n this is a test\n\nThe maximum segmented word length is 24 characters. Neither the unigram nor\nbigram data contain words exceeding that length. The corpus also excludes\npunctuation and all letters have been lowercased. Before segmenting text,\n`clean` is called to transform the input to a canonical form::\n\n >>> from zh_segment import clean\n >>> clean('She said, \"Python rocks!\"')\n 'shesaidpythonrocks'\n >>> segment('She said, \"Python rocks!\"')\n ['she', 'said', 'python', 'rocks']\n\nSometimes its interesting to explore the unigram and bigram counts\nthemselves. These are stored in Python dictionaries mapping word to count. ::\n\n >>> import zh_segment as ws\n >>> ws.load()\n >>> ws.UNIGRAMS['the']\n 23135851162.0\n >>> ws.UNIGRAMS['gray']\n 21424658.0\n >>> ws.UNIGRAMS['grey']\n 18276942.0\n\nAbove we see that the spelling `gray` is more common than the spelling `grey`.\n\nBigrams are joined by a space::\n\n >>> import heapq\n >>> from pprint import pprint\n >>> from operator import itemgetter\n >>> pprint(heapq.nlargest(10, ws.BIGRAMS.items(), itemgetter(1)))\n [('of the', 2766332391.0),\n ('in the', 1628795324.0),\n ('to the', 1139248999.0),\n ('on the', 800328815.0),\n ('for the', 692874802.0),\n ('and the', 629726893.0),\n ('to be', 505148997.0),\n ('is a', 476718990.0),\n ('with the', 461331348.0),\n ('from the', 428303219.0)]\n\nSome bigrams begin with ``. This is to indicate the start of a bigram::\n\n >>> ws.BIGRAMS[' where']\n 15419048.0\n >>> ws.BIGRAMS[' what']\n 11779290.0\n\nThe unigrams and bigrams data is stored in the `zh_segment_data` directory in\nthe `unigrams.txt` and `bigrams.txt` files respectively.\n\nReference and Indices\n---------------------\n\n* `zh_segment Documentation`_\n* `zh_segment at PyPI`_\n* `zh_segment at Github`_\n* `zh_segment Issue Tracker`_\n\n.. _`zh_segment Documentation`: https://github.com/wuhaifengdhu/zh_segment/tree/master/docs/docs\n.. _`zh_segment at PyPI`: https://pypi.python.org/pypi/zh_segment\n.. _`zh_segment at Github`: https://github.com/wuhaifengdhu/zh_segment\n.. _`zh_segment Issue Tracker`: https://github.com/wuhaifengdhu/zh_segment/issues\n\nzh_segment License\n-------------------\n\nCopyright 2017 Z&H\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n\n",
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