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<Paper uid="H05-1051">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 403-410, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Differentiating Homonymy and Polysemy in Information Retrieval</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
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    <Paragraph position="0"> Lexical ambiguity refers to words that share the same orthography but have different meanings (word senses). It can be sub-divided into two distinct types, homonymy and polysemy. Homonymy describes when two senses of a given word (or derivation) are distinct. Typically, they are separated by etymology and are therefore entirely unrelated in meaning. One classic example (Kilgarriff, 1992) is 'bat' as in an airborne mammal (from the Middle English word 'bakke' meaning flying rodent) vs. 'bat' as in an instrument used in the game of cricket (from the Celtic for stick or cudgel).</Paragraph>
    <Paragraph position="1"> There is no underlying relationship between these two meanings which have come about independently from differing root languages. Alternatively, polysemy describes where two senses of a word are related in that they share membership of a subsuming semantic classification. Consider the word 'mouth' as in a part of the body vs. 'mouth' as in the outlet of a river. Both meanings are subsumed by a higher concept (in this case they both describe an opening). Homonymy and polysemy are differentiated in most dictionaries by the major (homonyms) and minor (polysemes) entries for a given word. Where a lexical resource is described in terms of granularity a coarse-grained approach only differentiates between homonymy whereas a fine-grained approach also considers polysemy.</Paragraph>
    <Paragraph position="2"> The use of word sense disambiguation in Information Retrieval (IR) has been an active field of study for the past 30 years. Despite several failures (described in Sanderson, 2000) recent studies have begun to show increased retrieval effectiveness, particularly in Web retrieval. However, two key questions remain: (1) to what accuracy must disambiguation be performed in order to show increased retrieval effectiveness and (2) to what level of granularity should disambiguation be performed in order to maximize these gains? This study answers these questions by simulating the impact of ambiguity and its subsequent resolution on retrieval effectiveness.</Paragraph>
  </Section>
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