Pipelined biomedical event extraction rivaling joint learning

•An approach for pipelined event extraction consists of trigger identification, argument role recognition, and event construction.•BERT-based models are applied to three sub-tasks in biomedical event extraction.•N-ary relation extraction can effectively determine the validity of a candidate Binding event.•A pipelined biomedical event extraction rivaling joint learning. Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event’s context and its participants. The experimental results show that our method achieves promising results on the GE11 and GE13 corpora of the BioNLP shared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates that by significantly improving the performance of Binding events, the overall performance of the pipelined event extraction approach or even exceeds those of current joint learning methods.