Biomedical event trigger identification based on the bidirectional gated recurrent unit network

Abstract

Biomedical Events, which widely exist in biomedical literature, describe dynamical biological progress. Biomedical Event extraction plays a significant role in biomedical research, including biomedical event graph construction, medicine research and base construction. Event Trigger Identification, the very first step of event extraction, is to extract the words or phrases, usually verbs or verb groups, that trigger events from sentences or whole articles. Recently, in biomedical event extraction, the main approach to this task is based on traditional Machine Learning. However, this method relies heavily on human effort and expert experience. It can be very time-consuming. Thus, a more efficient method is needed in terms of decreasing labor and time costs. In this paper, we deal with the problem mentioned above to identify the biomedical event triggers in a new way by utilizing a deep learning-based model. Specifically, we use a Bidirectional Gated Recurrent Unit (Bi-GRU) network, an external version of the original Recurrent Neural Network (RNN), to encode the context, and a linear layer is used to classify the entities and predict the triggers. Finally, a test on the Multiple Level Event Extraction (MLEE) corpus gives a satisfying result (F1-score of around 78%).