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<Paper uid="C02-1126">
  <Title>Recovering latent information in treebanks</Title>
  <Section position="8" start_page="73" end_page="73" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> Even though researchers designing and implementing statistical parsing models have worked in the methodology shown in Figure 1 for several years now, most of the work has focused on finding effective features for the model component of the methodology, and on finding e ective statistical techniques for parameter estimation. However, there has been much behind-the-scenes work on the actual transformations, such as head finding, and most of this work has consisted of hand-tweaking existing heuristics. It is our hope that by introducing this new syntax, less toil will be needed to write non-terminal augmentation rules, and that human e ort will be lessened further by the use of unsupervised methods such as the one presented here to produce better models for parsing and tree augmentation.</Paragraph>
    <Paragraph position="1">  plified rule set. LR = labeled recall, LP = labeled precision; CB = average crossing brackets, 0 CB= no crossing brackets, 2 CB=two or fewer crossing brackets. All figures except CB are percentages.</Paragraph>
  </Section>
class="xml-element"></Paper>