Désambiguïsation lexicale de textes : efficacité qualitative et temporelle d’un algorithme à colonies de fourmis [Lexical disambiguation of texts: qualitative and temporal efficiency of an ant colony algorithm]

In this article, we present the notions of local and global algorithms, for the word sense disambiguation of texts. A local algorithm allows to calculate the semantic similarity between two lexical objects. Global algorithms propagate local measures at the upper level. We use this notion to compare an ant colony algorithm to other methods from the state of the art: a genetic algorithm and simulated annealing. Through their evaluation on a reference corpus, we show that the run-time efficiency of the ant colony algorithm makes the automated estimation of parameters possible and in turn the improvement of the quality results as well. Last, we study several late classifier fusion strategies over the results to improve the performance. MOTS-CLÉS : désambiguïsation lexicale fondée sur des similarités, algorithmes locaux/globaux, algorithmes à colonies de fourmis, algorithmes stochastiques d’optimisation.

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