Learning to Rank in XML Information Retrieval: Which Feature Improve the Best?

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dc.citation.epage340
dc.citation.spage336
dc.contributor.authorChaa, Messaoud
dc.contributor.authorNouali, Omar
dc.contributor.authorBal, Kamal
dc.date.accessioned2013-07-09T15:26:24Z
dc.date.available2013-07-09T15:26:24Z
dc.date.issued2012-08-23
dc.description.abstractThe augmented adoption of XML as the standard format for representing a document structure requires the development of tools to retrieve and rank effectively elements of the XML documents. It’s known that in information retrieval, considering multiple sources of relevance improves information retrieval. In this work some relevance features are defined and used in a learning to rank approach for XML information retrieval. Our aim is to combine theses features to derive good ranking function and show the impact of each feature in the relevance of XML element. Experiments on a large collection from the XML Information Retrieval evaluation campaign (INEX) showed good performance of the approach.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/204
dc.publisherIEEEfr_FR
dc.relation.ispartofICDIM 2012
dc.relation.ispartofseriesICDIM 2012;
dc.relation.pages336-340fr_FR
dc.relation.placeMacao, Chinefr_FR
dc.structureGénie Documentairefr_FR
dc.subjectXML information retrievalfr_FR
dc.subjectlearning-to-rankfr_FR
dc.subjectRanking SVMfr_FR
dc.subjectBM25fr_FR
dc.titleLearning to Rank in XML Information Retrieval: Which Feature Improve the Best?fr_FR
dc.typeConference paper
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