Here is a curated list of popular Python libraries for Natural Language Processing (NLP) that are often discussed in the ABC Compute Forum. These libraries are widely used for various NLP tasks and are a great starting point for developers and researchers.

  • NLTK (Natural Language Toolkit): This is the most popular library for NLP in Python. 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.

    NLTK

  • spaCy: spaCy is a fast and easy-to-use library for NLP. It's designed for production use and has a focus on information extraction, named entity recognition (NER), and other common NLP tasks.

    spaCy

  • Gensim: Gensim is a Python library for topic modeling and document similarity analysis. It provides algorithms like Latent Dirichlet Allocation (LDA) and Word2Vec, which are widely used in NLP for tasks like keyword extraction and sentiment analysis.

    Gensim

  • TextBlob: TextBlob is a simple library for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

    TextBlob

  • Transformers: Developed by Hugging Face, Transformers is an open-source library that provides state-of-the-art pre-trained models for NLP tasks. It includes models like BERT, GPT-2, and T5, which are used for tasks like text classification, language modeling, and translation.

    Transformers

For more resources and discussions on NLP with Python, you can visit the ABC Compute Forum.