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<Paper uid="C02-1061">
  <Title>Antonymy and Conceptual Vectors</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has presented a model of antonymy using the formalism of conceptual vectors. Our aim was to be able: (1) to spot antonymy if it was not given in de nition and thus provide an antonym as a result, (2) to use antonyms (discovered or given) to control or to ensure the coherence of an item vector, build by learning, which could be corrupted. In NLP, antonymy is a pivotal aspect, its major applications are thematic analysis of texts, construction of large lexical databases and word sense disambiguation.</Paragraph>
    <Paragraph position="1"> We grounded our research on a computable linguisitic theory being tractable with vectors for computational sake. This preliminary work on antonymy has also been conducted under the spotlight of symmetry, and allowed us to express antonymy in terms of conceptual vectors. These functions allow, from a vector and some contextual information, to compute an antonym vector. Some extensions have also been proposed so that these functions may be de ned and usable from lexical items. A measure has been identied to assess the level of antonymy between two items. The antonym vector construction is necessary for the selection of opposed lexical items in text generation. It also determines opposite ideas in some negation cases in analysis.</Paragraph>
    <Paragraph position="2"> Many improvements are still possible, the rst of them being revision of the VAC lists.</Paragraph>
    <Paragraph position="3"> These lists have been manually constructed by a reduced group of persons and should widely be validated and expanded especially by linguists.</Paragraph>
    <Paragraph position="4"> We are currently working on possible improvements of results through learning on a corpora.</Paragraph>
  </Section>
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