Word-sense induction
Open-knowledge reference entry
In computational linguistics, word-sense induction (WSI) or discrimination is an open problem of natural language processing, which concerns the automatic identification of the senses of a word (i.e. meanings). Given that the output of word-sense induction is a set of senses for the target word (sense inventory), this task is strictly related to that of word-sense disambiguation (WSD), which relies on a predefined sense inventory and aims to solve the ambiguity of words in context.
Approaches and methods
The output of a word-sense induction algorithm is a clustering of contexts in which the target word occurs or a clustering of words related to the target word. Three main methods have been proposed in the literature:
Context clustering
Word clustering
Co-occurrence graphs
Context clustering
The underlying hypothesis of this approach is that, words are semantically similar if they appear in similar documents, with in similar context windows, or in similar syntactic contexts. Each occurrence of a target word in a corpus is represented as a context vector. These context vectors can be either first-order vectors, which directly represent the context at hand, or second-order vectors, i.e., the contexts of the target word are similar if their words tend to co-occur together. The vectors are then clustered into groups, each identifying a sense of the target word. A well-known approach to context clustering is the Context-group Discrimination algorithm based on large matrix computation methods.
Word clustering
Word clustering is a different approach to the induction of word senses.
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This entry incorporates text from “Word-sense induction” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.