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<Paper uid="E93-1051">
  <Title>Lexical Disambiguation Using Constraint Handling In Prolog (CHIP) *</Title>
  <Section position="6" start_page="434" end_page="434" type="concl">
    <SectionTitle>
6 Conclusions and Future Directions
</SectionTitle>
    <Paragraph position="0"> It is difficult to make a straightforward comparison with other methods for lexical disambiguation, particularly \[Guthrie et al., 1992\]'s and \[Lesk, 1986\]'s, as there is no standard evaluation benchmark; but this approach seems to work reasonably well for small and medium scale disambiguation problems with a broadly similar success rate. We could try producing a much larger testbed for further comparative evaluations; but it is not clear how large this would have to he to become authoritative as an application-independent metric. Future enhancements to the approach incorporating the automatic use of the on-line subject codes and cross reference and subcategorisation systems of LDOCE can provide better results. Concerning CHIP, it provides a platform from which we can build in order to deal with large scale disambiguation; this could be used as an alternative to numerical optimisation techniques. The approach will involve the modelling of the problem in a combinatorial form so that constraint satisfaction logic programming \[Van Hentenryck, 1989\] can apply. For each sense of a word we can specify a set of constraints such as its subject code(s), or part-of-speech information or both. Forward checkable (or lookahead) rules can be introduced to decrease the number of possible senses of other words in advance (say, for example, that the 'economic' sense for the word 'bank' has been chosen, then only the 'economic' or 'neutral' senses of the 'arrange', 'overdraft' and 'account' will be taken into account). This suggests a dramatic reduction on the search space; CHIP offers all the necessary arithmetic and symbolic facilities for the implementation.</Paragraph>
    <Paragraph position="1"> Our experiments will be based on the use the machine version of LDOCE to verify the utility of this dictionary for the specific kind of applications we have in mind: the development methods and techniques that can assist large scale speech and handwriting recognition systems using semantic knowledge from already available resources (MRDs and corpora) \[Atwell et al., 1992\]. But the problem here is somewhat different: semantic constraints must be used for the correct choice between different candidate Ascii interpretations of a spoken or handwritten word.</Paragraph>
  </Section>
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