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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="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> \]in this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word from left to right. The history at each step of prediction is a sequence of partial parse krees covering the preceding words. First ore: model predicts the partial parse trees which have a dependency relation with the next word among them and then predicts the next word fi'om only the trees which have a dependency relation with the next word. Our: model is a generative stochastic model, thus this can be used not only as a parser but also as ~ language model of a speech recognizer. In our experiment, we prepared about 1,000 syntactically annotated Japanese sentences extracted fl'om a financial newspaper and estimated the parameters of our model. We built a parser based on our: model and tested it on approximately 10O sentences of the same newspaper. The accuracy of the dependency relation was 89.9%, the highest, accuracy level obtained by Japanese stochastic parsers.</Paragraph>
  </Section>
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