Natural language processing toolkit python

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README.md

Natural Language Toolkit (NLTK)

NLTK — the Natural Language Toolkit — is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.7, 3.8, 3.9, 3.10 or 3.11.

For documentation, please visit nltk.org.

Do you want to contribute to NLTK development? Great! Please read CONTRIBUTING.md for more details.

Have you found the toolkit helpful? Please support NLTK development by donating to the project via PayPal, using the link on the NLTK homepage.

If you publish work that uses NLTK, please cite the NLTK book, as follows:

Bird, Steven, Edward Loper and Ewan Klein (2009). Natural Language Processing with Python. O'Reilly Media Inc. 

Copyright (C) 2001-2023 NLTK Project

For license information, see LICENSE.txt.

AUTHORS.md contains a list of everyone who has contributed to NLTK.

• NLTK source code is distributed under the Apache 2.0 License.
• NLTK documentation is distributed under the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States license.
• NLTK corpora are provided under the terms given in the README file for each corpus; all are redistributable and available for non-commercial use.
• NLTK may be freely redistributed, subject to the provisions of these licenses.

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Natural Language Toolkit¶

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.

Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike. NLTK is available for Windows, Mac OS X, and Linux. Best of all, NLTK is a free, open source, community-driven project.

NLTK has been called “a wonderful tool for teaching, and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

Natural Language Processing with Python provides a practical introduction to programming for language processing. Written by the creators of NLTK, it guides the reader through the fundamentals of writing Python programs, working with corpora, categorizing text, analyzing linguistic structure, and more. The online version of the book has been been updated for Python 3 and NLTK 3. (The original Python 2 version is still available at https://www.nltk.org/book_1ed.)

Some simple things you can do with NLTK¶

Tokenize and tag some text:

>>> import nltk >>> sentence = """At eight o'clock on Thursday morning . Arthur didn't feel very good.""" >>> tokens = nltk.word_tokenize(sentence) >>> tokens ['At', 'eight', "o'clock", 'on', 'Thursday', 'morning', 'Arthur', 'did', "n't", 'feel', 'very', 'good', '.'] >>> tagged = nltk.pos_tag(tokens) >>> tagged[0:6] [('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'), ('Thursday', 'NNP'), ('morning', 'NN')] 

>>> entities = nltk.chunk.ne_chunk(tagged) >>> entities Tree('S', [('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'), ('Thursday', 'NNP'), ('morning', 'NN'), Tree('PERSON', [('Arthur', 'NNP')]), ('did', 'VBD'), ("n't", 'RB'), ('feel', 'VB'), ('very', 'RB'), ('good', 'JJ'), ('.', '.')]) 

>>> from nltk.corpus import treebank >>> t = treebank.parsed_sents('wsj_0001.mrg')[0] >>> t.draw() 

NB. If you publish work that uses NLTK, please cite the NLTK book as follows:

Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O’Reilly Media Inc.

Next Steps¶

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