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 BM25's 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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
He, B., & Ounis, I. (2005). Term frequency normalisation tuning for BM25 and DFR models. In Lecture Notes in Computer Science (Vol. 3408, pp. 200–214). Springer Verlag. https://doi.org/10.1007/978-3-540-31865-1_15
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