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Exploring a Continuous and Flexible Representation of the Lexicon

Abstract : We aim at showing that lexical descriptions based on multifactorial and continuous models can be used by linguists and lexicographers (and not only by machines) so long as they are provided with a way to efficiently navigate data collections. It is widely accepted that word description cannot be achieved without the analysis of a large number of contexts extracted from real corpora. The recent success of deep learning approaches has shown that discrete representations of the lexicon are no longer appropriate. Continuous models offer a better representation of word meaning, because they encode intuitively valid and cognitively plausible principles: semantic similarity is relative, context-sensitive and depends on multiple-cue integration. We propose tod demonstrate such a system that is usable both by machines and by humans.
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Contributor : Thierry Poibeau Connect in order to contact the contributor
Submitted on : Monday, December 26, 2016 - 9:22:36 AM
Last modification on : Thursday, March 17, 2022 - 10:08:40 AM
Long-term archiving on: : Tuesday, March 21, 2017 - 12:36:10 AM


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  • HAL Id : hal-01422479, version 1


Pierre Marchal, Thierry Poibeau. Exploring a Continuous and Flexible Representation of the Lexicon. 26th International Conference on Computational Linguistics (COLING 2016), Dec 2016, Osaka, Japan. pp.297-301. ⟨hal-01422479⟩



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