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<Paper uid="P06-1046">
  <Title>Scaling Distributional Similarity to Large Corpora</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the na&amp;quot;ive nearest-neighbour approach to comparing context vectors extracted from large corpora scales poorly (O(n2) in the vocabulary size).</Paragraph>
    <Paragraph position="1"> In this paper, we compare several existing approaches to approximating the nearest-neighbour search for distributional similarity. We investigate the trade-off between efficiency and accuracy, and find that SASH (Houle and Sakuma, 2005) provides the best balance.</Paragraph>
  </Section>
class="xml-element"></Paper>