IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING

Active learning has been successfully applied to many natural language processing tasks for obtaining annotated data in a cost-effective manner. We propose several extensions to an active learner that adopts the margin-based uncertainty sampling framework.

Experimental results on a cause detection problem involving the classification of aviation safety reports demonstrate the effectiveness of our extensions.