Information retrieval is a wide ranging area that deals with storage and retrieval of any kind of media. Not enought interest has been given over the past year in using natural languages morphology to retrieve text documents given a text query from a user. This thesis explores the different uses of morphological analysis to infer additional meaning from a given word in order to improve information retrieval systems. Considerations on morphological analysis for information retrieval is provided for French and English. A presentation of an alternative to stemming called Lightweight Morphology is given, which from a set of pattern matching rules, user defined rules and an exception tables can produce word expected inflections and derivations. Finally, in order to properly measure the interest of morphological analysis in text retrieval, a new measure to benchmark information retrieval systems, called "differential recall", is introduced. Lightweight morphology is compared to the Porter stemmer using state-of-the-art TREC benchmark and using differential recall measure.
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Information retrieval is a wide ranging area that deals with storage and retrieval of any kind of media. Not enought interest has been given over the past year in using natural languages morphology to retrieve text documents given a text query from a user. This thesis explores the different uses of morphological analysis to infer additional meaning from a given word in order to improve information retrieval systems. Considerations on morphological analysis for information retrieval is provided for French and English. A presentation of an alternative to stemming called Lightweight Morphology is given, which from a set of pattern matching rules, user defined rules and an exception tables can produce word expected inflections and derivations. Finally, in order to properly measure the interest of morphological analysis in text retrieval, a new measure to benchmark information retrieval systems, called "differential recall", is introduced. Lightweight morphology is compared to the Porter stemmer using state-of-the-art TREC benchmark and using differential recall measure.
Mikaël Roussillon graduated in 2004 from École des Mines de Saint-Étienne (France) and holds a Master of Computer Science from UNB (Canada). His master thesis research focused on improving information retrieval systems with morphological analysis, in collaboration with Sun Microsystems. He is currently a consultant in a French company.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. Natural Language Morphology Representation | An alternative to stemming for information retrieval improvement | Mikaël Roussillon | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639070132 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 101492001
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Information retrieval is a wide ranging area that deals with storage and retrieval of any kind of media. Not enought interest has been given over the past year in using natural languages morphology to retrieve text documents given a text query from a user. This thesis explores the different uses of morphological analysis to infer additional meaning from a given word in order to improve information retrieval systems. Considerations on morphological analysis for information retrieval is provided for French and English. A presentation of an alternative to stemming called Lightweight Morphology is given, which from a set of pattern matching rules, user defined rules and an exception tables can produce word expected inflections and derivations. Finally, in order to properly measure the interest of morphological analysis in text retrieval, a new measure to benchmark information retrieval systems, called 'differential recall', is introduced. Lightweight morphology is compared to the Porter stemmer using state-of-the-art TREC benchmark and using differential recall measure. N° de réf. du vendeur 9783639070132
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