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Focusing on the task of identifying event temporal status, we find thatevents directly or indirectly governing the target event in a dependency treeare most important contexts. Therefore, we extract dependency chains containingcontext events and use them as input in neural network models, whichconsistently outperform previous models using local context words as input.Visualization verifies that the dependency chain representation can effectivelycapture the context events which are closely related to the target event andplay key roles in predicting event temporal status.

Using Context Events in Neural Network Models for Event Temporal Status Identification