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<Paper uid="C04-1040">
  <Title>A Deterministic Word Dependency Analyzer Enhanced With Preference Learning</Title>
  <Section position="6" start_page="96" end_page="96" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> Dependency analysis is useful and annotation of word dependency seems easier than annotation of phrase labels. However, lack of phrase labels makes dependency analysis more difficult than phrase structure parsing. In this paper, we improved a deterministic dependency analyzer by adding a Root-Node Finder and a PP-Attachment Resolver. Preference Learning gave better scores than Collins' Model 3 parser for these subproblems, and the performance of the improved system is close to state-of-the-art phrase structure parsers. It turned out that SVM was unstable for PP attachment resolution whereas Preference Learning was not. We expect this method is also applicable to phrase structure parsers.</Paragraph>
  </Section>
class="xml-element"></Paper>