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Savoy, Jacques

Nom
Savoy, Jacques
Affiliation principale
Fonction
Professeur.e ordinaire
Email
jacques.savoy@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/282
_
0000-0002-4486-0067

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  • Publication
    Métadonnées seulement
    Combining multiple strategies for effective monolingual and cross-language retrieval
    (2004)
    Savoy, Jacques 
    This paper describes and evaluates different retrieval strategies that are useful for search operations on document collections written in various European languages, namely French, Italian, Spanish and German. We also suggest and evaluate different query translation schemes based on freely available translation resources. In order to cross language barriers, we propose a combined query translation approach that has resulted in interesting retrieval effectiveness. Finally, we suggest a collection merging strategy based on logistic regression that tends to perform better than other merging approaches.
  • Publication
    Métadonnées seulement
    Retrieval effectiveness on the web
    (2001)
    Savoy, Jacques 
    ;
    Picard, Justin
    Search engines play an essential role in the usability of Internet-based information systems and without them the web would certainly break down or, at the very least would develop at a much slower rate. Our main objective is to analyze and evaluate the retrieval effectiveness of various indexing and searching strategics on a new web text collection, using a rigorous evaluation methodology. Our second aim is to describe and evaluate different preprocessing techniques that might be implemented in order to improve retrieval effectiveness. As a third objective, this paper will evaluate whether or not hyperlinks may serve as useful sources of evidence in improving retrieval algorithms. (C) 2001 Elsevier Science Ltd. All rights reserved.
  • Publication
    Métadonnées seulement
    Statistical inference in retrieval effectiveness evaluation
    (1997)
    Savoy, Jacques 
    Evaluation methodology, and particularly its statistical tests associated, plays a central role in the information retrieval domain which maintains a strong empirical tradition. In an effort to evaluate the retrieval effectiveness of a search algorithm, this paper focuses on the average precision over a set of fixed recall values. After reviewing traditional evaluation methodology through the use of examples, this study suggests applying another statistical inference methodology called bootstrap, within which no particular assumption is needed about the distribution of the observations. Moreover, this scheme may be used to assert the accuracy of virtually any statistic, to build approximate confidence interval, and to verify whether a statistically significant difference exists between two retrieval schemes, even when dealing with a relatively small sample size. This study also suggests selecting the sample median rather than the sample mean in evaluating retrieval effectiveness where the justification for this choice is based on the nature of the information retrieval data. (C) 1997 Elsevier Science Ltd.