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<Paper uid="P03-1066">
  <Title>Unsupervised Learning of Dependency Structure for Language Modeling</Title>
  <Section position="11" start_page="24" end_page="24" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have presented a dependency language model that captures linguistic constraints via a dependency structure - a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Promising results of our experiments suggest that long-distance dependency relations can indeed be successfully exploited for the purpose of language modeling.</Paragraph>
    <Paragraph position="1"> There are many possibilities for future improvements. In particular, as discussed in Section 6, syntactic dependency structure is believed to capture useful information for informed language modeling, yet further improvements may be possible by incorporating non-syntax-based dependencies. Correlating the accuracy of the dependency parser as a parser vs. its utility in CER reduction may suggest a useful direction for further research.</Paragraph>
  </Section>
class="xml-element"></Paper>