Experiments with Monolingual, Bilingual, and Robust Retrieval
For our participation in the CLEF 2006 campaign, our first objective was to propose and evaluate a decompounding algorithm and a more aggressive stemmer for the Hungarian language. Our second objective was to obtain a better picture of the relative merit of various search engines for the French, Portuguese/Brazilian and Bulgarian languages. To achieve this we evaluated the test-collections using the Okapi approach, some of the models derived from the Divergence from Randomness (DFR) family and a language model (LM), as well as two vector-processing approaches. In the bilingual track, we evaluated the effectiveness of various machine translation systems for a query submitted in English and automatically translated into the French and Portuguese languages. After blind query expansion, the MAP achieved by the best single MT system was around 95% for the corresponding monolingual search when French was the target language, or 83% with Portuguese. Finally, in the robust retrieval task we investigated various techniques in order to improve the retrieval performance of difficult topics.
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