  Event Extraction plays an important role in information-extraction to
understand the world. Event extraction could be split into two subtasks: one is
event trigger extraction, the other is event arguments extraction. However, the
F-Score of event arguments extraction is much lower than that of event trigger
extraction, i.e. in the most recent work, event trigger extraction achieves
80.7%, while event arguments extraction achieves only 58%. In pipelined
structures, the difficulty of event arguments extraction lies in its lack of
classification feature, and the much higher computation consumption. In this
work, we proposed a novel Event Extraction approach based on multi-layer Dilate
Gated Convolutional Neural Network (EE-DGCNN) which has fewer parameters. In
addition, enhanced local information is incorporated into word features, to
assign event arguments roles for triggers predicted by the first subtask. The
numerical experiments demonstrated significant performance improvement beyond
state-of-art event extraction approaches on real-world datasets. Further
analysis of extraction procedure is presented, as well as experiments are
conducted to analyze impact factors related to the performance improvement.
