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<Paper uid="I05-2045">
  <Title>Unsupervised Feature Selection for Relation Extraction</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents an unsupervised relation extraction algorithm, which induces relations between entity pairs by grouping them into a &amp;quot;natural&amp;quot; number of clusters based on the similarity of their contexts. Stability-based criterion is used to automatically estimate the number of clusters. For removing noisy feature words in clustering procedure, feature selection is conducted by optimizing a trace based criterion sub-ject to some constraint in an unsupervised manner. After relation clustering procedure, we employ a discriminative category matching (DCM) to find typical and discriminative words to represent different relations. Experimental results show the effectiveness of our algorithm. null</Paragraph>
  </Section>
class="xml-element"></Paper>