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Savoy, Jacques
Nom
Savoy, Jacques
Affiliation principale
Fonction
Professeur.e ordinaire
Email
jacques.savoy@unine.ch
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Résultat de la recherche
Voici les éléments 1 - 10 sur 20
- PublicationAccès libreInformation retrieval with Hindi, Bengali, and Marathi languages: evaluation and analysis(2013)
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For the three studied languages, our experiments tend to show that IR models derived from Divergence from Randomness (DFR) paradigm tend to produce the best overall results. For these languages, our various experiments demonstrate also that either an aggressive stemming procedure or the trunc-n indexing approach produces better retrieval effectiveness when compared to other word-based or n-gram language-independent approaches. Applying the Z-score as data fusion operator after a blind-query expansion tends also to improve the MAP of the merged run over the best single IR system. - PublicationAccès libreRecherche d’information dans un corpus bruité (OCR)(2011)
; ; Dolamic, LjiljanaCet article désire mesurer la perte de performance lors de la recherche d'information dans une collection de documents scannés. Disposant d'un corpus sans erreur et de deux versions renfermant 5 % et 20 % d'erreurs en reconnaissance, nous avons évalué six modèles de recherche d'information basés sur trois représentations des documents (sac de mots, n-grammes, ou trunc-n) et trois enracineurs. Basé sur l'inverse du rang du premier document pertinent dépisté, nous démontrons que la perte de performance se situe aux environs de - 17 % avec un taux d'erreur en reconnaissance de 5 % et s'élève à – 46 % si ce taux grimpe à 20 %. La représentation par 4-grammes semble apporter une meilleure qualité de réponse avec un corpus bruité. Concernant l'emploi ou non d'un enracineur léger ou la pseudo-rétroaction positive, aucune conclusion définitive ne peut être tirée., This paper evaluates the retrieval effectiveness degradation when facing with noisy text corpus. With the use of a test-collection having the clean text, another version with around 5% error rate in recognition and a third with 20% error rate, we have evaluated six IR models based on three text representations (bag-of-words, n-grams, trunc-n) as well as three stemmers. Using the mean reciprocal rank as performance measure, we show that the average retrieval effectiveness degradation is around -17% when dealing with an error rate of 5%. This average decrease is around -46% when facing with an error rate of 20%. The representation by 4-grams tends to offer the best retrieval when searching with noisy text. Finally, we are not able to obtain clear conclusion about the impact of different stemming strategies or the use of blind-query expansion. - PublicationMétadonnées seulement
- PublicationMétadonnées seulementRetrieval effectiveness of machine translated queries(2010)
- PublicationAccès libreInfluence of language morphological complexity on information retrieval(2010)
; In this dissertation two aspects of information retrieval are elaborated. The frst involves the creation and evaluation of various linguistic tools for languages less studied than English, and in our case we have chosen to work with the two Slavic languages Czech and Russian, and three languages widely spoken on the Indian subcontinent, Hindi, Marathi and Bengali. To do so we compare various indexing strategies and IR models most likely to obtain the best possible performance. The second part involves an evaluation of the effectiveness of queries written in different languages when searching collections written in either English or French. To cross the language barriers we apply publicly available machine translation services, analyze the results and then explain the poor performances obtained by the translated queries. - PublicationAccès libreComparative Study of Indexing and Search Strategies for the Hindi, Marathi, and Bengali Languages(2010)
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. - PublicationAccès libreRetrieval Effectiveness of Machine Translated Queries(2010)
- PublicationAccès libreAd Hoc Retrieval with the Persian Language(2010)
- PublicationMétadonnées seulementIndexing and stemming approaches for the Czech language(2009)