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<Paper uid="P99-1054">
  <Title>Efficient probabilistic top-down and left-corner parsingt</Title>
  <Section position="6" start_page="425" end_page="425" type="concl">
    <SectionTitle>
5 Conclusions and Future Research
</SectionTitle>
    <Paragraph position="0"> We have examined several probabilistic predictive parser variations, and have shown the approach in general to be a viable one, both in terms of the quality of the parses, and the efficiency with which they are found. We have shown that the improvement of the grammars with non-local information not only results in better parses, but guides the parser to them much more efficiently, in contrast to dynamic programming methods. Finally, we have shown that the accuracy improvement that has been demonstrated with left-corner approaches can be attributed to the non-local information utilized by the method.</Paragraph>
    <Paragraph position="1"> This is relevant to the study of the human sentence processing mechanism insofar as it demonstrates that it is possible to have a model which makes explicit the syntactic relationships between items in the input incrementally, while still scaling up to broad-coverage.</Paragraph>
    <Paragraph position="2"> Future research will include: * lexicalization of the parser * utilization of fully connected trees for additional syntactic and semantic processing * the use of syntactic predictions in the beam for language modeling * an examination of predictive parsing with a left-branching language (e.g. German) In addition, it may be of interest to the psycholinguistic community if we introduce a time variable into our model, and use it to compare such competing sentence processing models as race-based and competition-based parsing.</Paragraph>
  </Section>
class="xml-element"></Paper>