Abstract
In this paper we propose an approach to discover functions for IR ranking from a space of simple closed-form mathematical functions. In general, all IR ranking models are based on two basic variables, namely, term frequency and document frequency. Here a grammar for generating all possible functions is defined which consists of the two above said variables and basic mathematical operations - addition, subtraction, multiplication, division, logarithm, exponential and square root. The large set of functions generated by this grammar is filtered by checking mathematical feasibility and satisfiability to heuristic constraints on IR scoring functions proposed by the community. Obtained candidate functions are tested on various standard IR collections and several simple but highly efficient scoring functions are identified. We show that these newly discovered functions are outperforming other state-of-the-art IR scoring models through extensive experimentation on several IR collections. We also compare the performance of functions satisfying IR constraints to those which do not, and show that the former set of functions clearly outperforms the latter one.
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Goswami, P., Moura, S., Gaussier, E., Amini, MR., Maes, F. (2014). Exploring the Space of IR Functions. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_31
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DOI: https://doi.org/10.1007/978-3-319-06028-6_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06027-9
Online ISBN: 978-3-319-06028-6
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