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Relieving Polysemy Problem for Synonymy Detection

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Progress in Artificial Intelligence (EPIA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5816))

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Abstract

In order to automatically identify noun synonyms, we propose a new idea which opposes classical polysemous representations of words to monosemous representations based on the “one sense per discourse” hypothesis. For that purpose, we apply the attributional similarity paradigm on two levels: corpus and document. We evaluate our methodology on well-known standard multiple choice synonymy question tests and evidence that it steadily outperforms the baseline.

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Dias, G., Moraliyski, R. (2009). Relieving Polysemy Problem for Synonymy Detection. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds) Progress in Artificial Intelligence. EPIA 2009. Lecture Notes in Computer Science(), vol 5816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04686-5_50

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  • DOI: https://doi.org/10.1007/978-3-642-04686-5_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04685-8

  • Online ISBN: 978-3-642-04686-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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