Term frequency normalisation tuning for BM25 and DFR models

He, B. and Ounis, I. (2005) Term frequency normalisation tuning for BM25 and DFR models. Lecture Notes in Computer Science, 3408, pp. 200-214. (doi: 10.1007/b107096)



Publisher's URL: http://dx.doi.org/10.1007/b107096


The term frequency normalisation parameter tuning is a crucial issue in information retrieval (IR), which has an important impact on the retrieval performance. The classical pivoted normalisation approach suffers from the collection-dependence problem. As a consequence, it requires relevance assessment for each given collection to obtain the optimal parameter setting. In this paper, we tackle the collection-dependence problem by proposing a new tuning method by measuring the normalisation effect. The proposed method refines and extends our methodology described in [7]. In our experiments, we evaluate our proposed tuning method on various TREC collections, for both the normalisation 2 of the Divergence From Randomness (DFR) models and the BM25s normalisation method. Results show that for both normalisation methods, our tuning method significantly outperforms the robust empirically-obtained baselines over diverse TREC collections, while having a marginal computational cost.

Item Type:Articles
Glasgow Author(s) Enlighten ID:He, Mr Ben and Ounis, Professor Iadh
Authors: He, B., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Copyright Holders:Copyright © 2005 Springer
First Published:First published in Lecture Notes in Computer Science 3408:200-214
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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