Evaluating active learning methods for annotating semantic predications.

objectivesThis study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect semantic predications in SemMedDB. MATERIALS AND

methodsWe evaluated 8 active learning strategies covering 3 types-uncertainty, representative, and combined-on 2 datasets of 6,000 total semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy called dynamic

resultsAll types of active learning (AL) methods beat the baseline on both datasets. Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. Our proposed AL method dynamic DISCUSSION: Our visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task.

conclusionActive learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemMedDB. Our proposed AL method demonstrated promising performance.