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A context vector model for information retrieval

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Planificación y Aprendizajees
dc.contributor.authorBillhardt, Holger
dc.contributor.authorBorrajo Millán, Daniel
dc.contributor.authorMaojo, Víctor
dc.date.accessioned2010-02-08T13:59:34Z
dc.date.available2010-02-08T13:59:34Z
dc.date.issued2002
dc.description.abstractIn the vector space model for information retrieval, term vectors are pair-wise orthogonal, that is, terms are assumed to be independent. It is well known that this assumption is too restrictive. In this article, we present our work on an indexing and retrieval method that, based on the vector space model, incorporates term dependencies and thus obtains semantically richer representations of documents. First, we generate term context vectors based on the co-occurrence of terms in the same documents. These vectors are used to calculate context vectors for documents. We present different techniques for estimating the dependencies among terms. We also define term weights that can be employed in the model. Experimental results on four text collections (MED, CRANFIELD, CISI, and CACM) show that the incorporation of term dependencies in the retrieval process performs statistically significantly better than the classical vector space model with IDF weights. We also show that the degree of semantic matching versus direct word matching that performs best varies on the four collections. We conclude that the model performs well for certain types of queries and, generally, for information tasks with high recall requirements. Therefore, we propose the use of the context vector model in combination with other, direct word-matching methods.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of the American Society for Information Science and Technology, 2002, vol. 53, n. 3, p. 236-249
dc.identifier.doi10.1002/asi.10032
dc.identifier.publicationfirstpage236
dc.identifier.publicationissue3
dc.identifier.publicationlastpage249
dc.identifier.publicationtitleJournal of the American Society for Information Science and Technology
dc.identifier.publicationvolume53
dc.identifier.urihttps://hdl.handle.net/10016/6790
dc.language.isoeng
dc.publisherWiley & Sons
dc.relation.publisherversionhttp://dx.doi.org/10.1002/asi.10032
dc.rights© Wiley Periodicals
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherVector space models
dc.subject.otherDocument retrieval
dc.subject.otherVector analysis
dc.subject.otherCo-occurrence analysis
dc.subject.otherContextual information
dc.titleA context vector model for information retrieval
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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