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<Paper uid="C04-1033">
  <Title>An NP-Cluster Based Approach to Coreference Resolution</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we have proposed a supervised learning-based approach to coreference resolution. Rather than mining the coreferential relationship between NP pairs as in conventional approaches, our approach does resolution by exploring the relationships between an NP and the coreferential clusters. Compared to individual NPs, coreferential clusters provide more information for rules learning and reference determination. In the paper, we flrst introduced the conventional NP-NP based approach and analyzed its limitation. Then we described in details the framework of our NP-Cluster based approach, including the instance representation, training and resolution procedures. We evaluated our approach in the biomedical domain, and the experimental results showed that our approach outperforms the NP-NP based approach in both recall (4.6%) and precision (1.3%).</Paragraph>
    <Paragraph position="1"> While our approach achieves better performance, there is still room for further improvement. For example, the approach just resolves an NP using the cluster information available so far. Nevertheless, the text after the NP would probably give important supplementary information of the clusters. The ignorance of such information may afiect the correct resolution of the NP. In the future work, we plan to work out more robust clustering algorithm to link an NP to a globally best cluster.</Paragraph>
  </Section>
class="xml-element"></Paper>