Abstract
Nowadays, the multimodal nature of the World Wide Web is an evidence. Web sites which include video files, pictures, music and text have become widespread. Furthermore, multimodal collections in several languages demand to apply multilingual information retrieval strategies. This paper describes a new retrieval technique applied on a multimodal and multilingual system that have been tested on two different multilingual image collections. The system applies several machine translators and implements some novel heuristics. These heuristics explore a variety of ways to combine the translations obtained from the given set of translators, and the configuration of the retrieval model by using different weighting functions, and also studying the effect of pseudo-relevance feedback (PRF) on this domain. Our results show interesting effects by these variations, allowing the determination of the parameters for the best retrieval model on this data and reporting the loss in performance on each language.
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García-Cumbreras, M.Á., Martín-Valdivia, M.T., Ureña-López, L.A., Díaz-Galiano, M.C., Montejo-Ráez, A. (2007). Using Translation Heuristics to Improve a Multimodal and Multilingual Information Retrieval System. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_55
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DOI: https://doi.org/10.1007/978-3-540-73400-0_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73399-7
Online ISBN: 978-3-540-73400-0
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