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<Paper uid="P99-1028">
  <Title>Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper deals with translation ambiguity and target polysemy problems together. Two monolingual balanced corpora are employed to learn word co-occurrence for translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution.</Paragraph>
    <Paragraph position="1"> Experiments show that the model achieves 62.92% of monolingual information retrieval, and is 40.80% addition to the select-all model.</Paragraph>
    <Paragraph position="2"> Combining the target polysemy resolution, the retrieval performance is about 10.11% increase to the model resolving translation ambiguity only.</Paragraph>
  </Section>
class="xml-element"></Paper>