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<Paper uid="C96-2163">
  <Title>Sense Classification of Verbal Polysemy based-on Bilingual Class/Class Association*</Title>
  <Section position="8" start_page="971" end_page="971" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> This pal)er proposed a t)ilingual class-based method for sense classification of verbal i)olysemy, which is based on the maximization of the bilingual class/frame association score. It achieved fairly high accuracy, although it is necessary to farther merge the clusters so that exactly one clus-ter corresponds to one hand-classified sense. We are planning to make experiments on sense classification without bilingual information to evaluate the e.lt'ectiveness of such bilingual information.</Paragraph>
    <Paragraph position="1"> li'or 9 verl)s, we made an ext)eriment on sense classification of verbal polysemy. We compared the result with the hand-classification and checked whether each cluster contained examples of only one hand-classitied sense (Table 3). In the table, 'CI.' and 'lEg.' indicate the numbers of ellis= ters and examples, respectively. The column 'One Sense (Jluster' means that each cluster contains examples of only one hand-classified sense, and the sub-eohmms 'CI.' and 'Eg.' list the number of SlLch (:lusters and the sum of examples contained in such clusters, respectively. We ewduated the accuracy of the method am the rate of the number of examples contained in one sense clusters as in the 'Eg.' sub-eohmm. This achieved 100% accuracy for four verbs out of the 9 verbs, and 93.3% in average. The coluinn 'Total C1./Hand-Classif.' indicates the ratio of the total number of clusters to the number of hand-classified senses, correspoading to the average number of clnsters into which one hand-classified sense is divided. Its a, verag% median, and standard deviation are 2.46, 1.80, and 1.06, respectively.</Paragraph>
    <Paragraph position="2"> The result of the experiment indicated that the t)r&lt;)posed sense classification method has achieved almost pure classification, while the result seems a little liner than hand-elassitieation. This is mainly cause&lt;l by the Net that clusters which correspond to the same hand-classified sense are separately located in the human-made thesaurus, and it is not easy to find exactly one representative class in the thesaurus (Utsuro, 11995). It is necessary to further merge the clusters so that exactly one cluster corresponds to one hand-classified sense.</Paragraph>
  </Section>
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