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Metadata-Version: 2.1
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Name: PyStemmer
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Version: 1.3.0
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Summary: Snowball stemming algorithms, for information retrieval
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Home-page: http://snowball.tartarus.org/
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Author: Richard Boulton
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Author-email: richard@tartarus.org
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Maintainer: Richard Boulton
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Maintainer-email: richard@tartarus.org
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License: ['MIT', 'BSD']
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Download-URL: http://snowball.tartarus.org/wrappers/PyStemmer-1.3.0.tar.gz
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Keywords: python,information retrieval,language processing,morphological analysis,stemming algorithms,stemmers
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Platform: any
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved :: MIT License
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Natural Language :: Danish
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Classifier: Natural Language :: Dutch
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Classifier: Natural Language :: English
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Classifier: Natural Language :: Finnish
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Classifier: Natural Language :: French
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Classifier: Natural Language :: German
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Classifier: Natural Language :: Italian
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Classifier: Natural Language :: Norwegian
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Classifier: Natural Language :: Portuguese
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Classifier: Natural Language :: Russian
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Classifier: Natural Language :: Spanish
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Classifier: Natural Language :: Swedish
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: C
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Classifier: Programming Language :: Other
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 2
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Classifier: Programming Language :: Python :: 2.6
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Classifier: Programming Language :: Python :: 2.7
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.2
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Classifier: Programming Language :: Python :: 3.3
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Classifier: Topic :: Database
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Classifier: Topic :: Internet :: WWW/HTTP :: Indexing/Search
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Classifier: Topic :: Text Processing :: Indexing
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Classifier: Topic :: Text Processing :: Linguistic
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Stemming algorithms
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PyStemmer provides access to efficient algorithms for calculating a
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"stemmed" form of a word. This is a form with most of the common
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morphological endings removed; hopefully representing a common
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linguistic base form. This is most useful in building search engines
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and information retrieval software; for example, a search with stemming
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enabled should be able to find a document containing "cycling" given the
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query "cycles".
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PyStemmer provides algorithms for several (mainly european) languages,
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by wrapping the libstemmer library from the Snowball project in a Python
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module.
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It also provides access to the classic Porter stemming algorithm for
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english: although this has been superceded by an improved algorithm, the
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original algorithm may be of interest to information retrieval
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researchers wishing to reproduce results of earlier experiments.
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