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<Paper uid="C94-2113">
  <Title>WORD SENSE AMBIGUATION: CLUSTERING RELATED SENSES</Title>
  <Section position="8" start_page="715" end_page="715" type="concl">
    <SectionTitle>
4. Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> Interestingly, the machinery used to identify common semantic threads among a polysemous word's senses was originally constructed with another purpose in mind -- namely, disambiguating LDOCE genus terms. As it turned out, exactly the same set of tests used to compare a word sense to the set of possible senses of its Hypernym proved usefid in comparing the different senses of a single word.</Paragraph>
    <Paragraph position="1"> While the current instantiation of the Clustering program relies partially on information which is idiosyncratic to LDOCE (e.g., Domain codes), most of the information it uses for inter-sense comparisons has been extracted from the text of their definitions.</Paragraph>
    <Paragraph position="2"> For this reason, the techniques we have described here are can be readily applied to other MRDs.</Paragraph>
    <Paragraph position="3"> In addition, we plan to experiment with augmenting the results of out' sense clustering with statistics derived from running a sense disambiguation program over a large fiee-text corpus. In particular, we are interested in discovering whether the &amp;quot;hard cases&amp;quot; encountered by this sense disambiguation program (i.e., those cases in which the program consistently has difficulty in choosing among two or more competing senses) correlate with cases of significant semantic overlap among senses, if this hypothesis is borne out, then information about which senses are difficult to distinguish in flee text can be used to help us establish the taxonomic relationships among the different senses of a polysemous word.</Paragraph>
    <Paragraph position="4"> Finally, the work we have described here has important implications for the task of merging multiple MRDs into a single lexical database. This task is greatly complicated by arbitrary sense divisions encountered in different dictionaries (see Atkins and Levin, 1988; Byrd, 1989). Consider the verb &amp;quot;mo(u)lt&amp;quot; again: since the single AHD3 sense for this word subsumes both LDOCE senses, no obvious mapping strategy is available. Should the AHD3 sense be mapped into just one of the LDOCE senses? Each of them? Or should the AHD3 sense be left separate, resulting in a merged lexical eutry with three separate entries? As more sources of infor,nation about word meanings arc folded in, this last strategy can only increase the complexity of semantic processing, since it will become more and more difficult to deternrine which of an ever-larger set of semantically-related senses is the appropriate one in a given context, Clustering offers a simple way to begin to approach this problem. By pooling and clustering senses for words fi'om both LDOCE and AHD3, we can provide a rough indication of the semantic iuterconnections between the two entries.</Paragraph>
    <Paragraph position="5"> As our techniques for automatically extracting semantic information from the text of definition and example sentences gradually improve, we expect our ability to automatically identify semantic overlaps and differences to improve as well.</Paragraph>
  </Section>
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