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<Paper uid="W00-1310">
  <Title>Nonlocal Language Modeling based on Context Co-occurrence Vectors</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a novel nonlocal lmlguage model which utilizes contextual information.</Paragraph>
    <Paragraph position="1"> A reduced vector space model calculated from co-occurrences of word pairs provides word co-occurrence vectors. The sum of word co-occurrence vectors represents tile context of a document, and the cosine similarity between the context vector and the word co-occurrence vectors represents the \]ong-distmlce lexical dependencies. Experiments on the Mainichi Newspaper corpus show significant improvement in perplexity (5.070 overall and 27.2% on target vocabulary)</Paragraph>
  </Section>
class="xml-element"></Paper>