Multinomial randomness models for retrieval with document fields

Plachouras, V. and Ounis, I. (2007) Multinomial randomness models for retrieval with document fields. Lecture Notes in Computer Science, 4425, pp. 28-39. (doi:10.1007/978-3-540-71496-5_6)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-540-71496-5_6

Abstract

Document fields, such as the title or the headings of a document, offer a way to consider the structure of documents for retrieval. Most of the proposed approaches in the literature employ either a linear combination of scores assigned to different fields, or a linear combination of frequencies in the term frequency normalisation component. In the context of the Divergence From Randomness framework, we have a sound opportunity to integrate document fields in the probabilistic randomness model. This paper introduces novel probabilistic models for incorporating fields in the retrieval process using a multinomial randomness model and its information theoretic approximation. The evaluation results from experiments conducted with a standard TREC Web test collection show that the proposed models perform as well as a state-of-the-art field-based weighting model, while at the same time, they are theoretically founded and more extensible than current field-based models.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ounis, Professor Iadh
Authors: Plachouras, V., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:1611-3349
Copyright Holders:Copyright © 2007 Springer
First Published:First published in Lecture Notes in Computer Science 4425:28-39
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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