Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features
TABLE II: Overall performance on blind test data.
['Model', 'Argument Identification(%) P', 'Argument Identification(%) R', 'Argument Identification(%) F', 'Argument Role(%) P', 'Argument Role(%) R', 'Argument Role(%) F']
We can see that our EE-DGCNN model, which is a pipelined system, achieves the best performance among all the compared systems. Compared to the best-reported model [yang-plmee], our model owns a great improvement over 3.2% for argument role assignment. This shows that our EE-DGCNN model which has richer local features and a better classifier is conducive to argument extraction. In addition, EE-DGCNN surpasses JRNN which is a joint method over 5.8% respectively. The experimental results demonstrate the efficacy of the proposed model EE-DGCNN.