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102 lines
3.6 KiB
ReStructuredText
102 lines
3.6 KiB
ReStructuredText
Natural Language Toolkit
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========================
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NLTK is a leading platform for building Python programs to work with human language data.
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It provides easy-to-use interfaces to `over 50 corpora and lexical
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resources <https://www.nltk.org/nltk_data/>`_ such as WordNet,
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along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning,
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wrappers for industrial-strength NLP libraries,
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and an active `discussion forum <https://groups.google.com/group/nltk-users>`_.
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Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation,
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NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike.
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NLTK is available for Windows, Mac OS X, and Linux. Best of all, NLTK is a free, open source, community-driven project.
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NLTK has been called "a wonderful tool for teaching, and working in, computational linguistics using Python,"
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and "an amazing library to play with natural language."
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`Natural Language Processing with Python <https://www.nltk.org/book/>`_ provides a practical
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introduction to programming for language processing.
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Written by the creators of NLTK, it guides the reader through the fundamentals
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of writing Python programs, working with corpora, categorizing text, analyzing linguistic structure,
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and more.
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The online version of the book has been been updated for Python 3 and NLTK 3.
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(The original Python 2 version is still available at `https://www.nltk.org/book_1ed <https://www.nltk.org/book_1ed>`_.)
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Some simple things you can do with NLTK
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---------------------------------------
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Tokenize and tag some text:
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>>> import nltk
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>>> sentence = """At eight o'clock on Thursday morning
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... Arthur didn't feel very good."""
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>>> tokens = nltk.word_tokenize(sentence)
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>>> tokens
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['At', 'eight', "o'clock", 'on', 'Thursday', 'morning',
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'Arthur', 'did', "n't", 'feel', 'very', 'good', '.']
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>>> tagged = nltk.pos_tag(tokens)
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>>> tagged[0:6]
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[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
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('Thursday', 'NNP'), ('morning', 'NN')]
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Identify named entities:
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>>> entities = nltk.chunk.ne_chunk(tagged)
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>>> entities
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Tree('S', [('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'),
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('on', 'IN'), ('Thursday', 'NNP'), ('morning', 'NN'),
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Tree('PERSON', [('Arthur', 'NNP')]),
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('did', 'VBD'), ("n't", 'RB'), ('feel', 'VB'),
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('very', 'RB'), ('good', 'JJ'), ('.', '.')])
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Display a parse tree:
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>>> from nltk.corpus import treebank
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>>> t = treebank.parsed_sents('wsj_0001.mrg')[0]
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>>> t.draw()
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.. image:: _static/images/tree.gif
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NB. If you publish work that uses NLTK, please cite the NLTK book as
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follows:
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Bird, Steven, Edward Loper and Ewan Klein (2009), *Natural Language Processing with Python*. O'Reilly Media Inc.
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Next Steps
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----------
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* `Sign up for release announcements <https://groups.google.com/group/nltk>`_
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* `Join in the discussion <https://groups.google.com/group/nltk-users>`_
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.. toctree::
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:maxdepth: 1
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:hidden:
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:caption: NLTK Documentation
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API Reference <api/nltk>
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Example Usage <howto>
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Module Index <py-modindex>
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Wiki <https://github.com/nltk/nltk/wiki>
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FAQ <https://github.com/nltk/nltk/wiki/FAQ>
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Open Issues <https://github.com/nltk/nltk/issues>
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NLTK on GitHub <https://github.com/nltk/nltk>
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.. toctree::
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:maxdepth: 1
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:hidden:
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:caption: Installation
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install
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data
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.. toctree::
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:maxdepth: 1
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:hidden:
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:caption: More
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news
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contribute
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team
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