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In supervised event detection, most of the mislabeling occurs between a smallnumber of confusing type pairs, including trigger-NIL pairs and siblingsub-types of the same coarse type. To address this label confusion problem,this paper proposes cost-sensitive regularization, which can force the trainingprocedure to concentrate more on optimizing confusing type pairs. Specifically,we introduce a cost-weighted term into the training loss, which penalizes moreon mislabeling between confusing label pairs. Furthermore, we also propose twoestimators which can effectively measure such label confusion based oninstance-level or population-level statistics. Experiments on TAC-KBP 2017datasets demonstrate that the proposed method can significantly improve theperformances of different models in both English and Chinese event detection.

Cost-sensitive Regularization for Label Confusion-aware Event Detection