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<Paper uid="W99-0903">
  <Title>Dual Distributional Verb Sense Disambiguation with Small Corpora and Machine Readable Dictionaries*</Title>
  <Section position="7" start_page="118" end_page="118" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have described an unsupervised sense disambiguation system using a small corpus and a MRD.</Paragraph>
    <Paragraph position="1"> Our system combines the advantages of corpus-based approaches (large number of word patterns) with those of the MRD-based approaches (data presorted by senses), by acquiring sense indicators from the MRD's usage examples as well as definitions and acquiring word co-occurrences from the corpus. Because the MRD's usage examples can be used as the sense-tagged instances, the sense indicators acquired from them are very useful for word sense disambiguation. In our system, Two nouns are considered similar even if they do not share any verbs if they appear as objects to similar verbs because the similarities between verbs simultaneously compute with the similarities between nouns. Thus, we can overcome effectively the problem of sparse data due to unobserved co-occurrences of words in the training corpus. Our experiments show that the results using the dual distribution and the MRD information lead to better performance on very sparse data.</Paragraph>
    <Paragraph position="2"> Our immediate plans are to test our system on various syntactic categories involving nouns as well as intransitive verbs and adjectives, and to suggest that different kinds of disambiguation procedures are needed depending on the syntactic category and other characteristics of the target word. Further- null more, we plan to build a large sense-tagged corpus, where the sense distinction is at the level of a dictionary in Korean. The sense-tagged corpus would be reused to achieve broad coverage, high accuracy word sense disambiguation.</Paragraph>
  </Section>
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