Exploiting argument information to improve biomedical event trigger identification via recurrent neural networks and supervised attention mechanisms

Abstract

In biomedical research, events revealing complex relations between entities play an important role. Event trigger identification is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in the previous work: (1) Traditional feature-based methods often rely on human ingenuity, which is a time-consuming process. Though most representation-based methods overcome this problem, these methods usually depend on local sentence representation features only within a window. (2) In current biomedical event trigger identification methods, arguments annotated in training set which can provide significant clues are completely ignored or exploited in an indirect manner. In this paper, we propose a Recurrent Neural Networks (RNN) based model considering argument information achieved via supervised attention mechanisms, which can automatically extract context features across the sentence and arguments clues. Meanwhile, we also introduce the dependency-based word embeddings in order to represent more dependency-based semantic information. Experimental results on the Multi Level Event Extraction (MLEE) corpus show that 1.14% improvement on F1-score is achieved by the proposed model when compared to the state-of-the-art approach, demonstrating the effectiveness of the proposed method.