nltk

Natural Language Toolkit

nltk Permissive license Apache License, Version 2.0 Active ★ 14,695 v3.10.3 released 2026-08-12

Install

nltk on PyPI

pip

pip install nltk

uv

uv add nltk

poetry

poetry add nltk

Package facts

License	Apache License, Version 2.0 (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	5 — defusedxml, click, joblib, regex, tqdm
Maintenance	actively maintained — 1 days since the last release
Last repo commit	2026-08-13
First released	2009-07-15
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: nltk-3.10.3-py3-none-any.whl

Keywords: NLP, CL, natural language processing, computational linguistics, parsing, tagging, tokenizing, syntax, linguistics, language, natural language, text analytics

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Human Machine InterfacesTopic :: Scientific/Engineering :: Information AnalysisTopic :: Text ProcessingTopic :: Text Processing :: FiltersTopic :: Text Processing :: GeneralTopic :: Text Processing :: IndexingTopic :: Text Processing :: Linguistic

About nltk

from the package's own PyPI description — quoted content, verbatim

Natural Language Toolkit (NLTK)

PyPI (image) CI (image)

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 from 3.10 up to the latest 3.14.

For documentation, please visit nltk.org.

Contributing

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

Donate

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

Citing

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

For license information, see...

Read as markdown · JSON record · Source repository · Homepage · Docs

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

NLTK is a comprehensive Python toolkit for natural language processing, offering modules for tokenization, parsing, tagging, and linguistic analysis alongside curated datasets and educational resources.

Installation is straightforward with low friction; the package is actively maintained with a release 1 day ago. Five runtime dependencies (defusedxml, click, joblib, regex, tqdm) are all well-established, and the wheel distribution ensures fast setup.

Apache License 2.0 is permissive and business-friendly, allowing commercial use and modification. Documentation and corpora carry separate terms (Creative Commons and corpus-specific licenses), but the core toolkit itself imposes minimal restrictions.

Usage

pip install nltk==3.10.3
import nltk
from nltk.tokenize import word_tokenize
tokens = word_tokenize('Hello, world!')

Python 3.10 or later is required; the package supports up to Python 3.14.

Verdict: NLTK is a mature, actively maintained production-grade NLP toolkit with no known vulnerabilities, permissive licensing, and minimal installation friction. Its broad ecosystem of corpora and tutorials makes it ideal for both research and education.

Needs verification

Whether offline corpus data requires separate download or is bundled with the wheel distribution
Performance characteristics when processing large-scale text datasets with the current dependency versions
Community adoption metrics beyond the maintenance signals provided

natural language processing pythonnlp tokenization parsing taggingtext analysis toolkitcomputational linguistics librarynltk natural language toolkitlinguistic analysis pythonnlp corpora datasets