This chapter focuses on unsupervised corpus-based methods of word sense discrimination that are knowledge-lean, and do not rely on external knowledge sources such as machine readable dictionaries, concept hierarchies, or sense-tagged text. They do not assign sense tags to words; rather, they discriminate among word meanings based on information found in unannotated corpora. This chapter reviews distributional approaches that rely on monolingual corpora and methods based on translational equivalence as found in word-aligned parallel corpora. These techniques are organized into type- and token-based approaches. The former identify sets of related words, while the latter distinguish among the senses of a word used in multiple contexts.
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Pedersen, T. (2007). Unsupervised Corpus-Based Methods for WSD. In: Agirre, E., Edmonds, P. (eds) Word Sense Disambiguation. Text, Speech and Language Technology, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4809-8_6
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