Discriminating among word meanings by identifying similar contexts

Amruta Purandare, Ted Pedersen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Word sense discrimination is an unsupervised clustering problem, which seeks to discover which instances of a word/s are used in the same meaning. This is done strictly based on information found in raw corpora, without using any sense tagged text or other existing knowledge sources. Our particular focus is to systematically compare the efficacy of a range of lexical features, context representations, and clustering algorithms when applied to this problem.

Original languageEnglish (US)
Title of host publicationProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-04)
Subtitle of host publicationSixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
Pages964-965
Number of pages2
StatePublished - Dec 9 2004
EventProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States
Duration: Jul 25 2004Jul 29 2004

Other

OtherProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
CountryUnited States
CitySan Jose, CA
Period7/25/047/29/04

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  • Cite this

    Purandare, A., & Pedersen, T. (2004). Discriminating among word meanings by identifying similar contexts. In Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-04): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) (pp. 964-965)