Greedy Active Learning for Reducing User Interaction

摘要

A method, system and computer-usable medium are disclosed for reducing user interaction when training an active learning system. Source input containing unlabeled instances and an input category are received. A Latent Semantic Analysis (LSA) similarity score, and a search engine score, are generated for each unlabeled instance, which in turn are used with the input category to rank the unlabeled instances. If a first threshold for negative instances has been met, a first unlabeled instance, having the highest ranking, is selected for annotation from the ranked collection of unlabeled instances and provided to a user for annotation with a positive label. If a second threshold for positive instances has been met, then second unlabeled instance, having the lowest ranking, is selected for annotation from the ranked collection of unannotated instances and automatically annotated with a negative label. The annotated instances are then used to train an active learning system.