In word sense disambiguation (WSD), several linguistic resources are commonly used to aid the algorithms in determining the correct sense of a word. These resources include:

  1. Traditional Dictionaries: Dictionaries provide information about word senses, definitions, and example sentences. A WSD algorithm can use this information to match the context of a word with its appropriate sense.

  2. Thesauri: Thesauri provide synonyms and related words for a given word. They can assist a WSD algorithm in determining the sense based on the similarity between the target word and its context.

  3. Lexical Knowledge Bases: These knowledge bases, such as WordNet, provide semantic relations between words, including hypernyms (is-a relationships), hyponyms (subtype relationships), and meronyms (part-whole relationships). WSD algorithms can leverage these relationships to infer the correct sense based on the hierarchy of meaning.

  4. Corpus Data: Large collections of text, known as corpora, can be used to train WSD algorithms. These corpora contain examples of words used in different contexts, enabling algorithms to learn patterns and associations between word senses and their surrounding words.

  5. Part-of-Speech (POS) Taggers: POS taggers assign grammatical categories (nouns, verbs, adjectives, etc.) to individual words in a text. WSD algorithms can use these tags to better disambiguate word senses, as different senses of a word may be associated with different POS categories.

  6. Sense-Tagged Corpora: Some corpora are manually annotated with sense tags, indicating the intended sense of ambiguous words. These resources serve as valuable training data for WSD algorithms, allowing them to learn from human-labeled examples.

  7. Machine-Readable Language Resources: Other machine-readable resources, such as ontologies and knowledge graphs, provide structured representations of knowledge and relationships. WSD algorithms can use these resources to access additional semantic information to aid in sense disambiguation.

  It's worth mentioning that WSD algorithms often combine multiple linguistic resources and employ various techniques, such as supervised learning, unsupervised learning, or knowledge-based approaches, to improve disambiguation accuracy. The choice and combination of resources depend on the specific WSD algorithm and its objectives.