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<Paper uid="W04-3224">
  <Title>A Distributional Analysis of a Lexicalized Statistical Parsing Model</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents some of the first data visualizations and analysis of distributions for a lexicalized statistical parsing model, in order to better understand their nature. In the course of this analysis, we have paid particular attention to parameters that include bilexical dependencies. The prevailing view has been that such statistics are very informative but su er greatly from sparse data problems. By using a parser to constrain-parse its own output, and by hypothesizing and testing for distributional similarity with back-o distributions, we have evidence that finally explains that (a) bilexical statistics are actually getting used quite often but that (b) the distributions are so similar to those that do not include head words as to be nearly indistinguishable insofar as making parse decisions. Finally, our analysis has provided for the first time an e ective way to do parameter selection for a generative lexicalized statistical parsing model.</Paragraph>
  </Section>
class="xml-element"></Paper>