Document (#43827)

Author

Campos, L.M. de
Fernández-Luna, J.M.
Huete, J.F.

Title

Implementing relevance feedback in the Bayesian network retrieval model

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.302-313

Year

2003

Abstract

Relevance Feedback consists in automatically formulating a new query according to the relevance judgments provided by the user after evaluating a set of retrieved documents. In this article, we introduce several relevance feedback methods for the Bayesian Network Retrieval ModeL The theoretical frame an which our methods are based uses the concept of partial evidences, which summarize the new pieces of information gathered after evaluating the results obtained by the original query. These partial evidences are inserted into the underlying Bayesian network and a new inference process (probabilities propagation) is run to compute the posterior relevance probabilities of the documents in the collection given the new query. The quality of the proposed methods is tested using a preliminary experimentation with different standard document collections.

Footnote

Beitrag eines Themenheftes: Mathematical, logical, and formal methods in information retrieval

Theme

Retrievalalgorithmen

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4. Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.; Martín-Dancausa, C.J.: Managing structured queries in probabilistic XML retrieval systems (2010) 0.19

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5. Larkey, L.S.; Connell, M.E.: Structured queries, language modelling, and relevance modelling in cross-language information retrieval (2005) 0.18

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