代价敏感的正则化标签混淆事件检测
Cost-sensitive Regularization for Label Confusion-aware Event Detection
摘要：
在监督事件检测中，大多数错误标记发生在少量混淆类型对之间，包括相同粗糙类型的兄弟子类型和相同粗糙类型的兄弟-NIL对。为了解决这个标签混淆问题，本文提出了代价敏感正则化，它可以迫使训练过程更多地集中在优化混淆类型对上。具体来说，我们在训练损失中引入了一个成本加权项，这会对混淆标签对之间的错误标签进行更多的惩罚。此外，我们还提出了两个估计，可以有效地衡量这种标签混淆的基础上，实例级或人口级的统计。在TAC-KBP 2017数据集上的实验表明，该方法可以显著提高不同模型在英语和汉语事件检测中的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
