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<Paper uid="J92-1001">
  <Title>Using Multiple Knowledge Sources for Word Sense Discrimination</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Artificial Intelligence Program
GE Research and Development Center
</SectionTitle>
    <Paragraph position="0"> This paper addresses the problem of how to identify the intended meaning of individual words in unrestricted texts, without necessarily having access to complete representations of sentences. To discriminate senses, an understander can consider a diversity of information, including syntactic tags, word frequencies, collocations, semantic context, role-related expectations, and syntactic restrictions. However, current approaches make use of only small subsets of this information.</Paragraph>
    <Paragraph position="1"> Here we will describe how to use the whole range of information. Our discussion will include how the preference cues relate to general lexical and conceptual knowledge and to more specialized knowledge of collocations and contexts. We will describe a method of combining cues on the basis of their individual specificity, rather than a fixed ranking among cue-types. We will also discuss an application of the approach in a system that computes sense tags for arbitrary texts, even when it is unable to determine a single syntactic or semantic representation for some sentences.</Paragraph>
  </Section>
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