Savoy, Jacques

2008, Dolamic, Ljiljana, Fautsch, Claire, Savoy, Jacques

2007, Fautsch, Claire, Savoy, Jacques

2005, Abdou, Samir, Ruck, Patrick, Savoy, Jacques

2004, Savoy, Jacques

2007, Dolamic, Ljiljana, Savoy, Jacques

2006, Abdou, Samir, Savoy, Jacques

2005, Abdou, Samir, Savoy, Jacques

2007, Abdou, Samir, Savoy, Jacques

2006, Savoy, Jacques, Abdou, Samir, Peters, Carol

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.

2005, Savoy, Jacques, Berger, Pierre-Yves, Peters, Carol, Gey, F. C., Gonzalo, Julio, Muller, H., Jones, G. J. F., Kluck, Michae, Magnini, B., DeRijke, M.

For our fifth participation in the CLEF evaluation campaigns, our first objective was to propose an effective and general stop-word list as well as a light stemming procedure for the Hungarian, Bulgarian and Portuguese (Brazilian) languages. Our second objective was to obtain a better picture of the relative merit of various search engines when processing documents in those languages. To do so we evaluated our scheme using two probabilistic models and five vector-processing approaches. In the bilingual track, we evaluated both the machine translation and bilingual dictionary approaches applied to automatically translate a query submitted in English into various target languages. Finally, using the GIRT corpora (available in English, German and Russian), we investigated the variations in retrieval effectiveness that resulted when we included or excluded manually assigned keywords attached to the bibliographic records (mainly comprising a title and an abstract).