Word Sense Disambiguation using NLP

Word Sense Disambiguation (WSD) is a critical task in Natural Language Processing (NLP) aimed at determining the correct meaning of a word based on its context within a text. We categorize WSD techniques into three main paradigms: knowledge-based methods, supervised learning approaches, and neural network-based models. Knowledge-based methods leverage lexical resources like WordNet and other semantic networks to disambiguate word senses by comparing context with predefined sense definitions. These methods often rely on similarity measures and heuristic rules but may struggle with the flexibility and variability of natural language. Supervised learning approaches utilize annotated corpora to train machine learning models that predict word senses. These methods, including decision trees, support vector machines, and ensemble techniques, have shown significant improvements with the advent of large-scale labelled datasets and feature engineering.