SenseClusters: Unsupervised clustering and labeling of similar contexts

Anagha Kulkarni, Ted Pedersen

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

13 Scopus citations

Abstract

SenseClusters is a freely available system that identifies similar contexts in text. It relies on lexical features to build first and second order representations of contexts, which are then clustered using unsupervised methods. It was originally developed to discriminate among contexts centered around a given target word, but can now be applied more generally. It also supports methods that create descriptive and discriminating labels for the discovered clusters.

Original languageEnglish (US)
Title of host publicationACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages105-108
Number of pages4
StatePublished - Dec 1 2005
Event43rd Annual Meeting of the Association for Computational Linguistics, ACL-05 - Ann Arbor, MI, United States
Duration: Jun 25 2005Jun 30 2005

Publication series

NameACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Other

Other43rd Annual Meeting of the Association for Computational Linguistics, ACL-05
CountryUnited States
CityAnn Arbor, MI
Period6/25/056/30/05

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