Comparison of similarity models for the relation discovery task

Authors:
Ben Hachey
Affiliations:
University of Edinburgh, Edinburgh
Venue:
LD '06 Proceedings of the Workshop on Linguistic Distances
Year:
2006

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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.