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<Paper uid="C04-1194">
  <Title>Discovering word senses from a network of lexical cooccurrences</Title>
  <Section position="9" start_page="3" end_page="3" type="concl">
    <SectionTitle>
8 Conclusion and future work
</SectionTitle>
    <Paragraph position="0"> In this article, we have presented a new method for discriminating and defining the senses of a word from a network of lexical cooccurrences. This method consists in applying an unsupervised clustering algorithm, in this case the SNN algorithm, to the cooccurrents of the word by relying on the relations that these cooccurrents have in the cooccurrence network. We have achieved a first evaluation based on the methodology defined in (Pantel and Lin, 2002). This evaluation has shown that in comparison with WordNet taken as a reference, the relevance of the discriminated senses is comparable to the relevance of Pantel and Lin's word senses. But it has also shown that the similarity between a discovered sense and a synset larity between a discovered sense and a synset of WordNet must be evaluated in our case by taking into account a larger set of semantic relations, especially those implicitly present in the glosses.</Paragraph>
    <Paragraph position="1"> Moreover, an evaluation based on the use of the built senses in an application such as query expansion is necessary to determine the actual interest of this kind of resources in comparison with a lexico-semantic network such as WordNet.</Paragraph>
  </Section>
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