Learning retrieval expert combinations with genetic algorithms

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dc.contributor.author Billhardt, Holger
dc.contributor.author Borrajo, Daniel
dc.contributor.author Maojo, Víctor
dc.date.accessioned 2010-02-04T12:20:50Z
dc.date.available 2010-02-04T12:20:50Z
dc.date.issued 2003-02
dc.identifier.bibliographicCitation International journal of uncertainty, fuzziness and knowledge-based systems, 2003, vol. 11, n. 1, p. 87-114
dc.identifier.issn 1793-6411 (online)
dc.identifier.issn 0218-4885 (print)
dc.identifier.uri http://hdl.handle.net/10016/6744
dc.description.abstract The goal of information retrieval (IR) is to provide models and systems that help users to identify the relevant documents to their information needs. Extensive research has been carried out to develop retrieval methods that solve this goal. These IR techniques range from purely syntax-based, considering only frequencies of words, to more semantics-aware approaches. However, it seems clear that there is no single method that works equally well on all collections and for all queries. Prior work suggests that combining the evidence from multiple retrieval experts can achieve significant improvements in retrieval effectiveness. A common problem of expert combination approaches is the selection of both the experts to be combined and the combination function. In most studies the experts are selected from a rather small set of candidates using some heuristics. Thus, only a reduced number of possible combinations is considered and other possibly better solutions are left out. In this paper we propose the use of genetic algorithms to find a suboptimal combination of experts for a document collection at hand. Our approach automatically determines both the experts to be combined and the parameters of the combination function. Because we learn this combination for each specific document collection, this approach allows us to automatically adjust the IR system to specific user needs. To learn retrieval strategies that generalize well on new queries we propose a fitness function that is based on the statistical significance of the average precision obtained on a set of training queries. We test and evaluate the approach on four classical text collections. The results show that the learned combination strategies perform better than any of the individual methods and that genetic algorithms provide a viable method to learn expert combinations. The experiments also evaluate the use of a semantic indexing approach, the context vector model, in combination with classical word matching techniques.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher World Scientific Publishing
dc.rights © World Scientific Publishing Company
dc.subject.other Information retrieval
dc.subject.other Data fusion
dc.subject.other Genetic algorithms
dc.subject.other Context vector model
dc.title Learning retrieval expert combinations with genetic algorithms
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1142/S0218488503001965
dc.subject.eciencia Informática
dc.identifier.doi 10.1142/S0218488503001965
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 87
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 114
dc.identifier.publicationtitle International journal of uncertainty, fuzziness and knowledge-based systems
dc.identifier.publicationvolume 11
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