This library provides Python implementations of various Word Sense Disambiguation (WSD) algorithms, targeting NLP researchers and developers needing to resolve word ambiguity in text. It offers multiple Lesk variants and similarity-based methods, integrating with NLTK and WordNet for comprehensive WSD solutions.

How It Works

PyWSD implements several WSD strategies, including the original Lesk algorithm, adapted/extended Lesk, and simple Lesk, which leverage dictionary definitions and examples for disambiguation. It also incorporates similarity-based methods like Wu-Palmer and information content measures, calculating semantic relatedness between word senses. This approach allows users to select the most suitable algorithm based on their specific task and data characteristics.

Quick Start & Requirements

• Install: pip install -U pywsd
• Prerequisites: pip install -U nltk, python -m nltk.downloader 'popular'
• Dependencies: Python 3.x, NLTK, WordNet corpus.
• Documentation: https://github.com/alvations/pywsd

Highlighted Details

• Implements multiple Lesk algorithm variants (Original, Adapted, Simple, Cosine).
• Includes similarity-based methods (Path Similarity, Information Content).
• Provides baseline disambiguation strategies (Random, First NLTK, Highest Lemma Counts).
• Supports all-words WSD and allows custom algorithm selection.

Maintenance & Community

• The project was last updated in 2014.
• Citation information and BibTeX entry are provided.

Licensing & Compatibility

• License: MIT.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The project has not been updated since 2014, potentially indicating limited ongoing maintenance or adoption of newer NLP techniques. Support is explicitly for Python 3.x; Python 2 users must use older versions (<= 1.1.7).