Harnessing the Redundant Results of Translation Spotting
Résumé
Translation spotting consists in automatically identifying the translations of a user query inside a bitext. This task, when it relies solely on statistical word alignment algorithms, fails to achieve excellent results. In this paper, we show that identifying the translations of a query during a first translation spotting stage provides relevant information that can be used in a second stage to improve the precision of the results. This method is similar to the relevance feedback used in the information retrieval domain to enhance retrieval.
Origine : Fichiers produits par l'(les) auteur(s)
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