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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
    Accès libre
    Comparative Study of Monolingual and Multilingual Search Models for Use with Asian Languages
    (2005)
    Savoy, Jacques 
    Based on the NTCIR-4 test-collection, our first objective is to present an overview of the retrieval effectiveness of nine vector-space and two probabilistic models that perform monolingual searches in the Chinese, Japanese, Korean, and English languages. Our second goal is to analyze the relative merits of the various automated and freely available toolsto translate the English-language topics into Chinese, Japanese, or Korean, and then submit the resultant query in order to retrieve pertinent documents written in one of the three Asian languages. We also demonstrate how bilingual searches could be improved by applying both the combined query translation strategies and data-fusion approaches. Finally, we address basic problems related to multilingual searches, in which queries written in English are used to search documents written in the English, Chinese, Japanese, and Korean languages.
  • Publication
    Accès libre
    Comparative Study of Indexing and Search Strategies for the Hindi, Marathi, and Bengali Languages
    Dolamic, Ljiljana
    ;
    Savoy, Jacques 
    The main goal of this article is to describe and evaluate various indexing and search strategies for the Hindi, Bengali, and Marathi languages. These three languages are ranked among the world’s 20 most spoken languages and they share similar syntax, morphology, and writing systems. In this article we examine these languages from an Information Retrieval (IR) perspective through describing the key elements of their inflectional and derivational morphologies, and suggest a light and more aggressive stemming approach based on them.
    In our evaluation of these stemming strategies we make use of the FIRE 2008 test collections, and then to broaden our comparisons we implement and evaluate two language independent indexing methods: the n-gram and trunc-n (truncation of the first n letters). We evaluate these solutions by applying our various IR models, including the Okapi, Divergence from Randomness (DFR) and statistical language models (LM) together with two classical vector-space approaches: tf idf and Lnu-ltc.
    Experiments performed with all three languages demonstrate that the I(ne)C2 model derived from the Divergence from Randomness paradigm tends to provide the best mean average precision (MAP). Our own tests suggest that improved retrieval effectiveness would be obtained by applying more aggressive stemmers, especially those accounting for certain derivational suffixes, compared to those involving a light stemmer or ignoring this type of word normalization procedure. Comparisons between no stemming and stemming indexing schemes shows that performance differences are almost always statistically significant. When, for example, an aggressive stemmer is applied, the relative improvements obtained are ≈28% for the Hindi language, ≈42% for Marathi, and ≈18% for Bengali, as compared to a no-stemming approach. Based on a comparison of word-based and language-independent approaches we find that the trunc-4 indexing scheme tends to result in performance levels statistically similar to those of an aggressive stemmer, yet better than the 4-gram indexing scheme. A query-by-query analysis reveals the reasons for this, and also demonstrates the advantage of applying a stemming or a trunc-4 indexing scheme.