Bayesian Information Retrieval: Preliminary Evaluation

Michelle Keim, David D. Lewis, David Madigan
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:303-318, 1997.

Abstract

Given a database of documents and a user’s query, how can we locate those documents that meet the user’s information needs? Because there is no precise definition of which documents in the database match the user’s query, uncertainty is inherent in the information retrieval process. Therefore, probability theory is a natural tool for formalizing the retrieval task. In this paper, we propose a Bayesian approach to one of the conventional probabilistic information retrieval models. We discuss the motivation for such a model, describe its implementation, and present some experimental results.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-keim97a, title = {Bayesian Information Retrieval: Preliminary Evaluation}, author = {Keim, Michelle and Lewis, David D. and Madigan, David}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {303--318}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/keim97a/keim97a.pdf}, url = {https://proceedings.mlr.press/r1/keim97a.html}, abstract = {Given a database of documents and a user’s query, how can we locate those documents that meet the user’s information needs? Because there is no precise definition of which documents in the database match the user’s query, uncertainty is inherent in the information retrieval process. Therefore, probability theory is a natural tool for formalizing the retrieval task. In this paper, we propose a Bayesian approach to one of the conventional probabilistic information retrieval models. We discuss the motivation for such a model, describe its implementation, and present some experimental results.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Bayesian Information Retrieval: Preliminary Evaluation %A Michelle Keim %A David D. Lewis %A David Madigan %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-keim97a %I PMLR %P 303--318 %U https://proceedings.mlr.press/r1/keim97a.html %V R1 %X Given a database of documents and a user’s query, how can we locate those documents that meet the user’s information needs? Because there is no precise definition of which documents in the database match the user’s query, uncertainty is inherent in the information retrieval process. Therefore, probability theory is a natural tool for formalizing the retrieval task. In this paper, we propose a Bayesian approach to one of the conventional probabilistic information retrieval models. We discuss the motivation for such a model, describe its implementation, and present some experimental results. %Z Reissued by PMLR on 30 March 2021.
APA
Keim, M., Lewis, D.D. & Madigan, D.. (1997). Bayesian Information Retrieval: Preliminary Evaluation. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:303-318 Available from https://proceedings.mlr.press/r1/keim97a.html. Reissued by PMLR on 30 March 2021.

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