An Event Structure-aware Generative Model for Biomedical Event Extraction

Biomedical Event Extraction (BEE) is a challenging task that involves modeling complex relationships between fine-grained entities in biomedical text. Most existing BEE models rely on classification methods that ignore label semantics and argument dependencies in the data. Although generative models that use prompts are increasingly being used for event extraction, they face two main challenges: creating effective prompts for the biomedical domain and dealing with events with complex structures in the text. To address these limitations, we propose GenBEE, a generative model enhanced with structure-aware prefixes for biomedical event extraction. GenBEE constructs event prompts that leverage knowledge distilled from large language models (LLMs), thereby incorporating both label semantics and argument dependency relationships. Additionally, GenBEE introduces a structural prefix learning module that generates structure-aware prefixes with structural prompts, enriching the generation process with structural features. Extensive experiments on three benchmark datasets demonstrate the effectiveness of GenBEE and it achieves state-of-the-art performance on the MLEE and GE11 datasets. Moreover, our analysis shows that the structural prefixes effectively bridge the gap between structural prompts and the representation space of generative models, enabling better integration of event structural information.