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<Paper uid="C96-1005">
  <Title>Word Sense Disambiguation using Conceptual Density</Title>
  <Section position="8" start_page="55" end_page="55" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> The automatic method for the disambiguation of nouns presented in this papcr is ready-usable in any general domain and on free-running text, given part of speech tags. It does not need any training and uses word sense tags from WordNet, an extensively used Icxieal data base.</Paragraph>
    <Paragraph position="1"> Conceptual Density has been used for other tasks apart from the disambiguation of free-running test. Its application for automatic spelling correction is outlined in tAgirre ct al. 94\]. It was also used on Computational Lexicography, enriching dictionary senses with semantic tags extracted from WordNet \[Rigau 9411, or linking bilingual dictionaries to WordNet \[Rigau and Agirre 96\].</Paragraph>
    <Paragraph position="2"> In the experiments, the algorithm disambiguated \['our texts (about 10,000 words long) of SemCor, a subset of the Brown corpus. The results were obtained automatically comparing the tags in SemCor with those computed by the algorithm, which would allow the comparison with other disambiguation methods.</Paragraph>
    <Paragraph position="3"> Two other methods, \[Sussna 93\] and \[Yarowsky 92\], were also tried on the same texts, showing that our algorithm performs better.</Paragraph>
    <Paragraph position="4"> Results are promising, considering the difficnlty of the task (free running text, large number of senses per word in WordNet), and the htck o1' any discourse structure of the texts. Two types el' results can be obtaincd: the specific scnse or a coarser, file level, tag.</Paragraph>
  </Section>
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