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<Paper uid="W98-1115">
  <Title>Edge-Based Best-First Chart Parsing *</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged ~'best&amp;quot; by some probabilistic figure of merit (FOM). Recent work has used probabilistic context-free grammars (PCFGs) to assign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to using a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finer-grained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results m that is, our parser achieves equivalent results using one twentieth the number of edges.</Paragraph>
    <Paragraph position="1"> Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing. null</Paragraph>
  </Section>
class="xml-element"></Paper>