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<Paper uid="P03-1023">
  <Title>Coreference Resolution Using Competition Learning Approach</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the single-candidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution.</Paragraph>
    <Paragraph position="1"> The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the single-candidate model.</Paragraph>
  </Section>
class="xml-element"></Paper>