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<Paper uid="H05-1063">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 499-506, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Mining Context Specific Similarity Relationships Using The World Wide Web</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We have studied how context specific web corpus can be automatically created and mined for discovering semantic similarity relationships between terms (words or phrases) from a given collection of documents (target collection). These relationships between terms can be used to adjust the standard vectors space representation so as to improve the accuracy of similarity computation between text documents in the target collection. Our experiments with a standard test collection (Reuters) have revealed the reduction of similarity errors by up to 50%, twice as much as the improvement by using other known techniques.</Paragraph>
  </Section>
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