Term-weighting approaches in automatic text retrieval
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An exploration of axiomatic approaches to information retrieval
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A new probabilistic retrieval model based on the dirichlet compound multinomial distribution
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Artificial Intelligence Review
Statistical Language Models for Information Retrieval A Critical Review
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A comparative study of methods for estimating query language models with pseudo feedback
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Discovering the discriminative views: measuring term weights for sentiment analysis
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Improving probabilistic information retrieval by modeling burstiness of words
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ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.