Abstract
In this paper, we explore several statistical methods to find solutions to the problem of query translation ambiguity. Indeed, we propose and compare a new possibilistic approach for query translation derived from a probabilistic one, by applying a classical probability-possibility transformation of probability distributions, which introduces a certain tolerance in the selection of word translations. Finally, the best words are selected based on a similarity measure. The experiments are performed on CLEF-2003 French-English CLIR collection, which allowed us to test the effectiveness of the possibilistic approach.
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Ben Romdhane, W., Elayeb, B., Bounhas, I., Evrard, F., Ben Saoud, N.B. (2013). A Possibilistic Query Translation Approach for Cross-Language Information Retrieval. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_9
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DOI: https://doi.org/10.1007/978-3-642-39482-9_9
Publisher Name: Springer, Berlin, Heidelberg
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