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- W958364461 abstract "In order to search within corpora written in two or more languages, the simplest and most effective approach is to translate the submitted request into the required lan- guage(s). To achieve this goal, we based our IR model on translation tools freely available on the Web. When comparing the retrieval effectiveness of manually and automatically translated requests, we found that human-based translation outperformed machine-based approaches. However, when we analyzed the query-by-query performance, we found query performances based on machine-based translations to vary a great deal. The question that then arises is whether or not we can predict the retrieval performance of a translated query and as a result we may thus select only the best translation(s). To respond to this, we designed and evaluated a predictive system based on the logistic regression, and used it to select the top most appropriate machine-based translations. An evaluation of this approach shows retrieval performance is better than using the best machine-based translation." @default.
- W958364461 created "2016-06-24" @default.
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- W958364461 date "2004-01-01" @default.
- W958364461 modified "2023-09-27" @default.
- W958364461 title "Recherche bilingue et multilingue d’information. Vers une sélection des bonnes traductions" @default.
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