Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

Event detection (ED) is aimed at detecting event occurrences and categorizing them. This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence. Thus, current approaches require both annotated triggers as well as event types in training data. Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the "most clearly" word from a sentence, particularly in longer sen-tences. To decrease manual effort, we evaluate event detection without triggers. We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks (TA-GCN) for event detection. Specifically, the task is identified as a multi-label classification problem. We first encode the input sentence using a novel type-aware neural network with attention mechanisms. Then, a Graph Convolutional Networks (GCN)-based multi -label classification model is exploited for event detection. Experimental results demonstrate the effectiveness.