Abstract
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.
Abstract (translated)
生物医学事件提取是一种从生物医学文本中获取事件信息的信息提取任务，其目标包括事件类型、触发器和相应的事件参与者。传统的生物医学事件提取通常采用流水线方法，包括触发器识别、参与方角色识别和事件构建，或使用特定规则或机器学习。在本文中，我们提出了一种基于BERT预训练模型的n元关系提取方法，以构建绑定事件，以捕捉事件上下文和参与者的语义信息。实验结果表明，我们的方法在BioNLP共享任务中使用F1分数为63.14%和59.40%的数据集时取得了很好的结果。这表明，通过显著提高绑定事件的表现，流水线事件提取方法的总体性能甚至超过了当前的联合学习方法。
URL
https://arxiv.org/abs/2403.12386
