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Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group-for example, the proportion of a document collection with positive sentiment. Previous work has focused on aggregating and adjusting discriminative individual classifiers to obtain prevalence point estimates. But imperfect classifier accuracy ought to be reflected in uncertainty over the predicted prevalence for scientifically valid inference. In this work, we present (1) adoi:10.18653/v1/d18-1487 fatcat:xjtnrigwjjf7zo3lks3w67rgm4