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<Paper uid="W06-2503">
  <Title>Relating WordNet Senses for Word Sense Disambiguation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The granularity of word senses in current general purpose sense inventories is often too fine-grained, with narrow sense distinctions that are irrelevant for many NLP applications. This has particularly been a problem with WordNet which is widely used for word sense disambiguation (WSD). There have been several attempts to group WordNet senses given a number of different information sources in order to reduce granularity. We propose relating senses as a matter of degree to permit a softer notion of relationships between senses compared to fixed groupings so that granularity can be varied according to the needs of the application. We compare two such approaches with a gold-standard produced by humans for this work. We also contrast this gold-standard and another used in previous research with the automatic methods for relating senses for use with back-off methods for WSD.</Paragraph>
  </Section>
class="xml-element"></Paper>