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<Paper uid="H92-1094">
  <Title>AUGMENTING WITH SLOT FILLER RELEVANCY SIGNATURES DATA</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> Human readers can reliably identify many relevant texts merely by skimming the texts for domain-specific cues.</Paragraph>
    <Paragraph position="1"> These quick relevancy judgements require two steps: (1) recognizing an expression that is highly relevant to the given domain, e.g. &amp;quot;were killed&amp;quot; in the domain of terrorism, and (2) verifying that the context surrounding the expression is consistent with the relevancy guidelines for the domain, e.g. &amp;quot;5 soldiers were killed by guerrillas&amp;quot; is not consistent with the terrorism domain since victims of terrorist acts must be civilians 1. The Relevancy Signatures Algorithm attempts to simulate the first step in this process by deriving reliable relevancy cues from a corpus of training texts and using these cues to quickly identify new texts that are highly likely to be relevant. But since this algorithm makes no attempt to look beyond the relevancy cues, it will occasionally misclassify texts when the surrounding context contains additional information that makes the text irrelevant.</Paragraph>
    <Paragraph position="2"> As a first attempt to address this problem, we developed a variation of the Relevancy Signatures Algorithm that augments the relevancy signatures with slot filler information. While relevancy signatures classify texts based upon the presence of case frames, augmented relevancy signatures classify texts on the basis of case frame instantiations, Experimental results show that the augmented relevancy signatures can achieve higher precision than relevancy signatures alone while still maintaining significant levels of recall.</Paragraph>
  </Section>
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