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<Paper uid="P99-1005">
  <Title>Distributional Similarity Models: Clustering vs. Nearest Neighbors</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Distributional similarity is a useful notion in estimating the probabilities of rare joint events.</Paragraph>
    <Paragraph position="1"> It has been employed both to cluster events according to their distributions, and to directly compute averages of estimates for distributional neighbors of a target event. Here, we examine the tradeoffs between model size and prediction accuracy for cluster-based and nearest neighbors distributional models of unseen events.</Paragraph>
  </Section>
class="xml-element"></Paper>