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<Paper uid="W05-1011">
  <Title>Approximate Searching for Distributional Similarity</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Distributional similarity requires large volumes of data to accurately represent infrequent words. However, the nearest-neighbour approach to finding synonyms suffers from poor scalability. The Spatial Approximation Sample Hierarchy (SASH), proposed by Houle (2003b), is a data structure for approximate nearest-neighbour queries that balances the efficiency/approximation trade-off. We have intergrated this into an existing distributional similarity system, tripling efficiency with a minor accuracy penalty.</Paragraph>
  </Section>
class="xml-element"></Paper>