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<Paper uid="C02-1020">
  <Title>Extracting Word Sequence Correspondences with Support Vector Machines</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper proposes a learning and extracting method of word sequence correspondences from non-aligned parallel corpora with Support Vector Machines, which have high ability of the generalization, rarely cause over-fit for training samples and can learn dependencies of features by using a kernel function. Our method uses features for the translation model which use the translation dictionary, the number of words, part-of-speech, constituent words and neighbor words. Experiment results in which Japanese and English parallel corpora are used archived 81.1 % precision rate and 69.0 % recall rate of the extracted word sequence correspondences. This demonstrates that our method could reduce the cost for making translation dictionaries.</Paragraph>
  </Section>
class="xml-element"></Paper>