<?xml version="1.0" standalone="yes"?>
<Paper uid="C00-2098">
  <Title>A Context-Sensitive Model for Probabilistie LR Parsing of Spoken Language with Transformation-Based Postproeessing</Title>
  <Section position="8" start_page="681" end_page="682" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this article we have extended probabilistic shift-reduce parsing to be more context-sensitive than previous works and have demonstrated that a bigger context improves the performance of a probabilistic shift-reduce parser. It was shown that our model is suitable to parse utterances of the Verbmobil domain in three different languages. It was also shown that the exact match rate of a probabilistic parser can be improved significantly using a symbolic transformation-based post-processing step.</Paragraph>
    <Paragraph position="1"> Our method of learning tree transforlnations has generated first promising results but it is based on the mapping of whole trees to whole trees. It could be a direction of further research to extend this process of learning transformations on smaller  (sub-)structures like single phrases. That should improve generalization and hel t ) improving the exact match rate on the difficult dolnain of parsing spontaneously spoken utterances.</Paragraph>
  </Section>
class="xml-element"></Paper>