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• W2951855710 abstract "In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection." @default.
• W2951855710 created "2019-06-27" @default.
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• W2951855710 date "2019-01-01" @default.
• W2951855710 modified "2023-10-16" @default.
• W2951855710 title "Cost-sensitive Regularization for Label Confusion-aware Event Detection" @default.
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• W2951855710 doi "https://doi.org/10.18653/v1/p19-1521" @default.
• W2951855710 hasPublicationYear "2019" @default.
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