Biomedical Event Extraction (BEE) is a challenging task that involves modeling complex relationships between fine-grained entities in biomedical text. BEE has traditionally been formulated as a classification problem. With the recent technological advancements in large language models (LLMs), generation-based models that cast event extraction as a sequence generation problem have attracted much attention from the NLP research communities. However, current generative models often overlook the importance of cross-instance information from complex event structures such as nested events and overlapping events, which contribute to over 20% of the events in the benchmark datasets. In this paper, we propose an event structure-aware generative model named GenBEE, which can capture complex event structures in biomedical text for biomedical event extraction. In particular, GenBEE constructs event prompts that distill knowledge from LLMs for incorporating both label semantics and argument dependency relationships into the proposed model. In addition, GenBEE also generates prefixes with event structural prompts to incorporate structural features for improving the model's overall performance. We have evaluated the proposed GenBEE model on three widely used biomedical event extraction benchmark datasets, namely MLEE, GE11, and PHEE. Experimental results show that GenBEE has achieved state-of-the-art performance on the MLEE and GE11 datasets, and achieved competitive results when compared to the state-of-the-art classification-based models on the PHEE dataset.

翻译：生物医学事件抽取（BEE）是一项具有挑战性的任务，涉及对生物医学文本中细粒度实体间的复杂关系进行建模。传统上，BEE被构建为一个分类问题。随着大语言模型（LLMs）技术的最新进展，将事件抽取视为序列生成问题的基于生成的模型已引起了自然语言处理研究社区的广泛关注。然而，当前的生成式模型往往忽视了来自复杂事件结构（如嵌套事件和重叠事件）的跨实例信息的重要性，这些事件在基准数据集中占比超过20%。本文提出了一种名为GenBEE的事件结构感知生成式模型，该模型能够捕捉生物医学文本中的复杂事件结构以进行生物医学事件抽取。具体而言，GenBEE构建事件提示，从大语言模型中提炼知识，将标签语义和论元依赖关系同时融入所提出的模型。此外，GenBEE还生成带有事件结构提示的前缀，以整合结构特征，从而提升模型的整体性能。我们在三个广泛使用的生物医学事件抽取基准数据集（即MLEE、GE11和PHEE）上评估了所提出的GenBEE模型。实验结果表明，GenBEE在MLEE和GE11数据集上取得了最先进的性能，并在PHEE数据集上与基于分类的最先进模型相比取得了具有竞争力的结果。