How reliable are the results of large-scale information retrieval experiments?
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Generating beta variates with nonintegral shape parameters
Communications of the ACM
A statistical method for system evaluation using incomplete judgments
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Robust test collections for retrieval evaluation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Bayesian Statistics
Introduction to Bayesian Statistics
Document categorization in legal electronic discovery: computer classification vs. manual review
Journal of the American Society for Information Science and Technology
Assessor error in stratified evaluation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Sequential testing in classifier evaluation yields biased estimates of effectiveness
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Towards minimizing the annotation cost of certified text classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Recall, the proportion of relevant documents retrieved, is an important measure of effectiveness in information retrieval, particularly in the legal, patent, and medical domains. Where document sets are too large for exhaustive relevance assessment, recall can be estimated by assessing a random sample of documents, but an indication of the reliability of this estimate is also required. In this article, we examine several methods for estimating two-tailed recall confidence intervals. We find that the normal approximation in current use provides poor coverage in many circumstances, even when adjusted to correct its inappropriate symmetry. Analytic and Bayesian methods based on the ratio of binomials are generally more accurate but are inaccurate on small populations. The method we recommend derives beta-binomial posteriors on retrieved and unretrieved yield, with fixed hyperparameters, and a Monte Carlo estimate of the posterior distribution of recall. We demonstrate that this method gives mean coverage at or near the nominal level, across several scenarios, while being balanced and stable. We offer advice on sampling design, including the allocation of assessments to the retrieved and unretrieved segments, and compare the proposed beta-binomial with the officially reported normal intervals for recent TREC Legal Track iterations.