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<Paper uid="W05-1524">
  <Title>Vancouver, October 2005. c(c)2005 Association for Computational Linguistics TFLEX: Speeding up Deep Parsing with Strategic Pruning</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> This paper presents a method for speeding up a deep parser through backbone extraction and pruning based on CFG ambiguity packing.1 The TRIPS grammar is a wide-coverage grammar for deep natural language understanding in dialogue, utilized in 6 different application domains, and with high coverage and sentence-level accuracy on human-human task-oriented dialogue corpora (Dzikovska, 2004).</Paragraph>
    <Paragraph position="1"> The TRIPS parser uses a best- rst beam search algorithm and a chart size limit, both of which are a form of pruning focused on nding an n-best list of interpretations. However, for longer sentences limiting the chart size results in failed parses, while increasing the chart size limits signi cantly impacts the parsing speed.</Paragraph>
    <Paragraph position="2"> It is possible to speed up parsing by implementing faster uni cation algorithms, but this requires considerable implementation effort. Instead, we developed a new parser, TFLEX, which uses a simpler technique to address ef ciency issues. TFLEX combines the TRIPS grammar with the fast parsing technologies implemented in the LCFLEX parser (Ros*e and Lavie, 2001). LCFLEX is an all-paths parser which uses left-corner prediction and ambiguity packing, and which was shown to be ef cient on other uni cation augmented context-free grammars. We describe a way to transfer the TRIPS grammar to LCFLEX, and a pruning method which achieves signi cant improvements in both speed and coverage compared to the original TRIPS parser.</Paragraph>
  </Section>
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