A knowledge-based recommender scoring annotators by past accuracy, mood, and fatigue improves annotation correctness and reduces model uncertainty in a simulated active learning setup.
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Improving annotator selection in Active Learning using a mood and fatigue-aware Recommender System
A knowledge-based recommender scoring annotators by past accuracy, mood, and fatigue improves annotation correctness and reduces model uncertainty in a simulated active learning setup.