Contents

nltk 3.7

0

Natural Language Toolkit

Natural Language Toolkit

Stars: 10959, Watchers: 10959, Forks: 2635, Open Issues: 226

The nltk/nltk repo was created 12 years ago and was last updated 5 hours ago.
The project is extremely popular with a mindblowing 10959 github stars!

How to Install nltk

You can install nltk using pip

pip install nltk

or add it to a project with poetry

poetry add nltk

Package Details

Author
NLTK Team
License
Apache License, Version 2.0
Homepage
https://www.nltk.org/
PyPi
https://pypi.org/project/nltk/
GitHub Repo
https://github.com/nltk/nltk

Classifiers

  • Scientific/Engineering
  • Scientific/Engineering/Artificial Intelligence
  • Scientific/Engineering/Human Machine Interfaces
  • Scientific/Engineering/Information Analysis
  • Text Processing
  • Text Processing/Filters
  • Text Processing/General
  • Text Processing/Indexing
  • Text Processing/Linguistic
No  nltk  pypi packages just yet.

Errors

A list of common nltk errors.

Code Examples

Here are some nltk code examples and snippets.

GitHub Issues

The nltk package has 226 open issues on GitHub

  • ConditionalFreqDist.add is quadratic time-ish
  • Fix LC cutoff policy of text tiling
  • From TreebankWordDetokenizer, the detokenize adds/subtracts spaces to/from special characters
  • Potential bug in sentence tokenizer since 3.6.6
  • Add extended open multilingual wordnet reader
  • is_writable function produces the wrong boolean output when run on AWS Lambda with EFS storage attached
  • Potential accidental capture of loop variable in Boxer
  • nltk + gunicorn + preloading + subprocess.check_output crashes worker on macOS
  • In CI, refresh nltk_data cache if the hash of index.xml differs from the cached hash
  • Create Markdown corpus readers
  • Support extended open multilingual wordnet

See more issues on GitHub

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Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. The core data structures of Keras are layers and models. The philosophy is to keep simple things simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code via subclassing). Deep learning for humans.