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<Paper uid="C00-1081">
  <Title>A Stochastic Parser Based on a Structural Word Prediction Model Shinsuke MORI, Masafumi NISHIMURA, Nobuyasu ITOH,</Title>
  <Section position="7" start_page="563" end_page="563" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we have presented a stochastic language model based on dependency structure. This model treats a sentence as a word sequence and predicts each word from left to right. &amp;quot;The history at each step of prediction is a sequence of partial parse trees covering the preceding words. To predict a word, ore: model first selects the partial parse trees that have a dependency relation with the word, and then predicts the next word from the selected partial parse trees. We also presented an algorithm %r lexicalization. We lmilt parsers based on the POS-based model and its lexicalized version, whose parameters are estimated from 1,072 sentences of a financial newspaper. We tested the parsers on 119 sentences Dom the same newspaper, which we.re excluded fl:om the learning. The accuracy of the dependency relation of the lexicalized parser was 89.9%, the highest obtained by any Japanese stochastic parser.</Paragraph>
  </Section>
class="xml-element"></Paper>