Abstract
In this paper, we propose a new automatic query translation disambiguation using bilingual proximity-based approach. This approach combines a traditional bilingual dictionary and parallel bilingual corpus to build a bilingual semantic dictionary of contexts (BSDC) and identify the suitable translation of a word using a proximity matching model. Besides, it uses and extends an existing probabilistic semantic distance to compute similarities between words using a bilingual semantic graph of the traditional bilingual dictionary and the BSDC. We experiment and compare this approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. Our experiments highlighted the performance of our bilingual proximity-based approach compared to both the known efficient probabilistic and the possibilistic ones, for both long and short queries and using different assessment metrics.
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Ben Romdhane, W., Elayeb, B., Bellamine Ben Saoud, N. (2019). Automatic Query Translation Disambiguation Using Bilingual Proximity-Based Approach. In: Debruyne, C., Panetto, H., Guédria, W., Bollen, P., Ciuciu, I., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2018 Workshops. OTM 2018. Lecture Notes in Computer Science(), vol 11231. Springer, Cham. https://doi.org/10.1007/978-3-030-11683-5_25
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DOI: https://doi.org/10.1007/978-3-030-11683-5_25
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