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<Paper uid="P95-1037">
  <Title>Statistical Decision-Tree Models for Parsing*</Title>
  <Section position="6" start_page="281" end_page="282" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> Regardless of what techniques are used for parsing disambiguation, one thing is clear: if a particular piece of information is necessary for solving a disambiguation problem, it must be made available to the disambiguation mechanism. The words in the sentence are clearly necessary to make parsing decisions, and in some cases long-distance structural information is also needed. Statistical models for  tence length for Wall Street Journal experiments.</Paragraph>
    <Paragraph position="1"> parsing need to consider many more features of a sentence than can be managed by n-gram modeling techniques and many more examples than a human can keep track of. The SPATTER parser illustrates how large amounts of contextual information can be incorporated into a statistical model for parsing by applying decision-tree learning algorithms to a large annotated corpus.</Paragraph>
  </Section>
class="xml-element"></Paper>