From Table 4, we can see that: 1.
specifically, for each ED instance x = (t, c), we generate a positive lexical-enhanced training instance (t, c, t) with label (y, 1), and n negative instances 4 (t, c, w) with label (y, 0), where w is a word randomly sampled from context c. For each ED instance x = (t, c) in the train dataset D, the event classifier loss is: and the lexical binary classifier loss is: the loss function of lexical-enhanced ∆-learning is: By adding the auxiliary lexical classification task, this learning algorithm will ensure the learned representation be both event-related and lexicalrelevant.