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
Probabilistic models of information retrieval based on measuring the divergence from randomness
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
Text classification with kernels on the multinomial manifold
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Information-based models for ad hoc IR
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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We study the impact of concavity in IR models and propose to use a generalized logarithm function, the n-logarithm to weight words in documents. We extend the family of information based Information Retrieval (IR) models with this function. We show that that concavity is indeed an important property of IR models. Experiments conducted for IR tasks, Latent Semantic Indexing and Text Categorization show improvements.