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