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<Paper uid="W06-1105">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Comparison of Similarity Models for the Relation Discovery Task</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
  </Section>
class="xml-element"></Paper>