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<Paper uid="W03-1013">
  <Title>Log-Linear Models for Wide-Coverage CCG Parsing</Title>
  <Section position="9" start_page="8" end_page="8" type="concl">
    <SectionTitle>
8 Conclusions and Further Work
</SectionTitle>
    <Paragraph position="0"> Table 1 gives the overall statistics for the model estimation process, and compares them with Miyao and Tsujii (2002). These numbers represent the largest-scale parsing model of which we are aware. Parsing and model estimation on this scale introduce a number of interesting theoretical and computational challenges. We have demonstrated how packed charts and feature forests can be combined to meet the theoretical challenges. We have also described an MPI implementation of GIS which solves the computational challenges. These techniques are necessary for discriminative estimation techniques applied to wide-coverage parsing.</Paragraph>
    <Paragraph position="1"> We have just begun the process of evaluating parsing performance using the same test data as Clark et al. (2002). We are especially interested in the effectiveness of incorporating long-range dependencies as features, which CCG was designed to handle and for which we expect a log-linear model to be particularly effective.</Paragraph>
  </Section>
class="xml-element"></Paper>