A Discriminative Possibilistic Approach for Query Translation Disambiguation

We propose, assess and compare in this paper a new discriminative possibilistic query translation (QT) disambiguation approach using both a bilingual dictionary and a parallel text corpus in order to overcome some drawbacks of the dictionary-based techniques. In this approach, the translation relevance of a given source query term is modeled by two measures: the possible relevance allows rejecting irrelevant translations, whereas the necessary relevance makes it possible to reinforce the translations not eliminated by the possibility. We experiment this new 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 new discriminative possibilistic approach compared to both the probabilistic and the probability-to-possibility transformation-based approaches, especially for short queries and using different assessment metrics.

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