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 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.
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He, B., Ounis, I. (2005). Term Frequency Normalisation Tuning for BM25 and DFR Models. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_15
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DOI: https://doi.org/10.1007/978-3-540-31865-1_15
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