Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
A formal study of information retrieval heuristics
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The BNB distribution for text modeling
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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We first present in this paper an analytical view of heuristic retrieval constraints which yields simple tests to determine whether a retrieval function satisfies the constraints or not. We then review empirical findings on word frequency distributions and the central role played by burstiness in this context. This leads us to propose a formal definition of burstiness which can be used to characterize probability distributions wrt this phenomenon. We then introduce the family of information-based IR models which naturally captures heuristic retrieval constraints when the underlying probability distribution is bursty and propose a new IR model within this family, based on the log-logistic distribution. The experiments we conduct on three different collections illustrate the good behavior of the log-logistic IR model: it significantly outperforms the Jelinek-Mercer and Dirichlet prior language models on all three collections, with both short and long queries and for both the MAP and the precision at 10 documents. It also outperforms the InL2 DFR model for the MAP, and yields results on a par with it for the precision at 10.