SenseClusters: unsupervised clustering and labeling of similar contexts

Authors:
Anagha Kulkarni;Ted Pedersen
Affiliations:
University of Minnesota, Duluth, MN;University of Minnesota, Duluth, MN
Venue:
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Year:
2005

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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.