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<Paper uid="A00-1020">
  <Title>Multilingual Coreference Resolution</Title>
  <Section position="8" start_page="147" end_page="148" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have introduced a new data-driven method for multilingual coreference resolution, implemented in the SWIZZLE system. The results of this method are encouraging since they show clear improvements over monolingual coreference resolution. Currently, we are also considering the effects of a bootstrapping algorithm for multilingual coreference resolution. Through this procedure we would learn concurrently semantic consistency knowledge and better performing heuristic rules. To be able to develop such a learning approach, we must first develop a method for automatic recognition of multilingual referential expressions.</Paragraph>
    <Paragraph position="1">  We also believe that a better performance evaluation of SidIZZLE can be achieved by measuring its impact on several complex applications. We intend to analyze the performance of SIdIZZLE when it is used as a module in an IE system, and separately in a Question/Answering system.</Paragraph>
    <Paragraph position="2"> Acknowledgements This paper is dedicated to the memory of our friend Megumi Kameyama, who inspired this work.</Paragraph>
  </Section>
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