Biomedical Event Trigger Identification via Multiple Self-attention Mechanisms

Biomedical events represent the interrelationship between various biomedical entities. Extracting biomedical events from biomedical works is a research hotspot. Among all event extraction methods, biomedical event trigger recognition is a crucial stage. However, there is insufficient capability to extract semantic information from complex biological texts in the current research models. It is necessary to design a new model to enhance the extraction ability. Herein, we propose a biomedical event trigger identification model based on a multi-layer self-attention mechanism. Our model applies the currently popular multilevel self-attention structure, which can imitate how humans browse text. By configuring weights for each word, it can better extract the critical information contained in biomedical text. To validate the effectiveness of the self-attention structure, a series of experiments are conducted on the Multi-Level Event Extraction (MLEE) dataset. The as-obtained results uncover that the proposed model has achieved comparable properties in the task of biomedical event trigger identification.