Word Sense Disambiguation: How Computers Learn to Read Context Like Humans

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Word Sense Disambiguation (WSD) is a core Natural Language Processing (NLP) challenge focused on determining the correct meaning of a word based on its context. Most high-frequency English words carry multiple meanings — 'run' has over thirty distinct senses — making lexical ambiguity a fundamental property of language rather than an exception. Traditional WSD systems take a target word and its surrounding text as input, then match it to the correct definition using structured resources like WordNet, which organises words into labeled synonym sets. For example, a WSD system distinguishes 'operated' in a surgical context from its use in an industrial one, despite the word being identical. Modern NLP has evolved beyond fixed dictionary labels, increasingly representing word meaning as continuous numerical vectors in high-dimensional space to capture nuance more flexibly.

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