Word Sense Disambiguation (WSD)
Also known as: WSD, sense tagging, Sözcük Anlamı Belirsizlik Giderme (WSD)
Word sense disambiguation (WSD) is the natural-language-processing task of choosing the correct meaning of a polysemous word from its context. Surveyed by Navigli (2009), it resolves which sense of a many-meaning word applies in a given sentence, improving the quality of information retrieval, machine translation, and question answering.
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When to use it
WSD applies when you have text containing words with multiple meanings and a lexical knowledge resource — WordNet or a target-language ontology — that lists their senses, together with a defined context window. It suits explanatory and classification work on text data, and a modest corpus (around 20 units) is enough to begin. Without an available sense inventory for the language, knowledge-based WSD cannot run.
Strengths & limitations
- Resolves polysemy so downstream tasks read the intended meaning rather than a surface string.
- Improves the quality of information retrieval, machine translation, and question answering.
- Knowledge-based approaches work directly from a lexical resource without needing large labelled training sets.
- Requires an available WordNet or target-language ontology; languages without a sense inventory cannot be disambiguated this way.
- Accuracy depends on how well the defined context window captures the disambiguating evidence.
- Fine-grained sense distinctions in a resource can be hard to separate even with rich context.
Frequently asked
What knowledge resource does WSD need?
Knowledge-based WSD relies on a lexical resource that lists the senses of words — WordNet for English, or an equivalent ontology for the target language. The resource supplies the candidate senses and their glosses that the method compares against the context.
How does the context window matter?
The context window is the set of neighbouring words used to decide which sense applies. It is a defined parameter: too narrow and it misses the disambiguating signal, too wide and it adds irrelevant words. Choosing an appropriate window is central to the method.
What is the Lesk algorithm?
Lesk is a classic knowledge-based approach that compares the words in the context window against the dictionary gloss of each candidate sense and picks the sense with the greatest overlap. It works directly from the lexical resource without labelled training data.
Does WSD work for languages other than English?
Yes, provided a sense inventory or ontology exists for that language. Applying an English resource such as WordNet to text in another language will not produce correct senses; the resource must match the corpus language.
Sources
- Navigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2), Article 10, 1-69. DOI: 10.1145/1459352.1459355 ↗
- Mihalcea, R. (2006). Knowledge-Based Methods for Word Sense Disambiguation. Computational Linguistics. link ↗
How to cite this page
ScholarGate. (2026, June 1). Word Sense Disambiguation (WSD). ScholarGate. https://scholargate.app/en/text-mining/word-sense-disambiguation
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