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<Paper uid="H93-1052">
  <Title>ONE SENSE PER COLLOCATION</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> The use of collocations to resolve lexical ambiguities is certainly not a new idea. The first approaches to sense disambiguation, such as \[Kelly and Stone 1975\], were based on simple hand-built decision tables consisting almost exclusively of questions about observed word associations in specific positions. Later work from the AI community relied heavily upon selectional restrictions for verbs, although primarily in terms of features exhibited by their arguments (such as +DRINKABLE) rather than in terms of individual words or word classes. More recent work \[Brown et al. 1991\]\[Hearst 1991\] has utilized a set of discrete local questions (such as word-to-the-right) in the development of statistical decision procedures. However, a strong trend in recent years is to treat a reasonably wide context window as an unordered bag of independent evidence points. This technique from information retrieval has been used in neural networks, Bayesian discriminators, and dictionary definition matching. In a comparative paper in this volume \[Leacock et al. 1993\], all three methods under investigation used words in wide context as a pool of evidence independent of relative position. It is perhaps not a coincidence that this work has focused almost exclusively on nouns, as will be shown in Section 6.2. In this study we will return again to extremely local sources of evidence, and show that models of discrete syntactic relationships have considerable advantages.</Paragraph>
    <Paragraph position="1"> *This research was supported by an NDSEG Fellowship and by DARPA grant N00014-90-J-1863. The author is also affiliated with the Linguistics Research Department of AT&amp;T Bell Laboratories, and greatly appreciates the use of its resources in support of this work. He would also like to thank Eric Bfill, Bill Gale, Libby Levison, Mitch Marcus and Philip Resnik for their valuable feedback.</Paragraph>
  </Section>
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