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<Paper uid="W06-2922">
  <Title>Experiments with a Multilanguage Non-Projective Dependency Parser</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Parsing natural language is an essential step in several applications that involve document analysis, e.g. knowledge extraction, question answering, summarization, filtering. The best performing systems at the TREC Question Answering track employ parsing for analyzing sentences in order to identify the query focus, to extract relations and to disambiguate meanings of words.</Paragraph>
    <Paragraph position="1"> These are often demanding applications, which need to handle large collections and to provide results in a fraction of a second. Dependency parsers are promising for these applications since a dependency tree provides predicate-argument relations which are convenient for use in the later stages. Recently statistical dependency parsing techniques have been proposed which are deterministic and/or linear (Yamada and Matsumoto, 2003; Nivre and Scholz, 2004). These parsers are based on learning the correct sequence of Shift/Reduce actions used to construct the dependency tree. Learning is based on techniques like SVM (Vapnik 1998) or Memory Based Learning (Daelemans 2003), which provide high accuracy but are often computationally expensive. Kudo and Matsumoto (2002) report a two week learning time on a Japanese corpus of about 8000 sentences with SVM. Using Maximum Entropy (Berger, et al. 1996) classifiers I built a parser that achieves a throughput of over 200 sentences per second, with a small loss in accuracy of about 2-</Paragraph>
  </Section>
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