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<Paper uid="P98-2191">
  <Title>Maximum Entropy Model Learning of the Translation Rules</Title>
  <Section position="8" start_page="1174" end_page="1174" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have described an approach to learn the translation rules from parallel corpora based on the maximum entropy method. As feature functions, we have defined two models, one with co-occurrence information and the other with morphological information.</Paragraph>
    <Paragraph position="1"> As computational cost associated with this method is too expensive, we have proposed several methods to suppress the overhead in order to realize the system. We had experiments for each model of features, and the result showed the effectiveness of this method, especially for the model of features with co-occurrence and morphological information.</Paragraph>
  </Section>
class="xml-element"></Paper>