Extreme value theory applied to document retrieval from large collections
Information Retrieval
Predicting Query Performance by Query-Drift Estimation
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
On score distributions and relevance
ECIR'07 Proceedings of the 29th European conference on IR research
Standard deviation as a query hardness estimator
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Modeling score distributions in information retrieval
Information Retrieval
Improved query performance prediction using standard deviation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Measuring the ability of score distributions to model relevance
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
On theoretically valid score distributions in information retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
On the inference of average precision from score distributions
Proceedings of the 21st ACM international conference on Information and knowledge management
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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The standard deviation of scores in the top k documents of a ranked list has been shown to be significantly correlated with average precision and has been the basis of a number of query performance predictors. In this paper, we outline two hypotheses that aid in understanding this correlation. Using score distribution (SD) models with known parameters, we create a large number of document rankings using Monte Carlo simulation to test the validity of these hypotheses.