Cost-sensitive Regularization for Label Confusion-aware Event Detection
Table 3: Error rates (CNN) on trigger words on the Chinese test set with Sampling(SP) and CR-INS(CR).
['Error Rate (%)', '[BOLD] SP', '[BOLD] CR', 'Δ']
To clearly show where the improvement of our method comes from, we compared the mislabeling made by Sampling and our CR-INS method. We can first see that trigger/NIL mislabeling and sibling sub-types mislabeling make up most of errors of CE baseline. This further verifies our motivation. Besides, cost-sensitive regularization significantly reduces these two kinds of errors without introducing more other types of mislabeling, which clearly demonstrates the effectiveness of our method.