Comparison of similarity models for the relation discovery task

Ben Hachey

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

We present results on the relation discovery task, which addresses some of the shortcomings of supervised relation extraction by applying minimally supervised methods. We describe a detailed experimental design that compares various configurations of conceptual representations and similarity measures across six different subsets of the ACE relation extraction data. Previous work on relation discovery used a semantic space based on a term-by-document matrix. We find that representations based on term co-occurrence perform significantly better. We also observe further improvements when reducing the dimensionality of the term co-occurrence matrix using probabilistic topic models, though these are not significant.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Linguistic Distances
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages25-34
Number of pages10
ISBN (Print)1932432833
Publication statusPublished - 2006
Externally publishedYes
EventWorkshop on Linguistic Distances - Sydney
Duration: 23 Jul 200623 Jul 2006

Workshop

WorkshopWorkshop on Linguistic Distances
CitySydney
Period23/07/0623/07/06

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