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 theperformance of Binding events, the overall performance of the pipelined event extraction approach or even exceeds those of current joint learning methods.

翻译：生物医学事件抽取是一项从生物医学文本中获取事件的信息抽取任务，其目标包括事件的类型、触发词以及事件中涉及的相应论元。传统的生物医学事件抽取通常采用流水线方法，包含触发词识别、论元角色识别，最终通过特定规则或机器学习进行事件构建。本文提出一种基于BERT预训练模型的n元关系抽取方法，用于构建Binding事件，以捕获事件上下文及其参与者的语义信息。实验结果表明，该方法在BioNLP共享任务的GE11和GE13语料库上取得了令人满意的结果，F1分数分别为63.14%和59.40%。这表明，通过显著提升Binding事件的性能，流水线事件抽取方法的整体性能能够达到甚至超越当前联合学习方法。