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)
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Publisher's URL: http://dx.doi.org/10.1007/b107096
Abstract
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 |
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Status: | Published |
Refereed: | Yes |
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 |
Publisher: | Springer |
ISSN: | 1611-3349 |
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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