A Structure-aware Generative Model for Biomedical Event Extraction

TL;DR

This paper introduces GenBEE, a structure-aware generative model for biomedical event extraction that effectively captures complex event structures, outperforming existing models on multiple benchmark datasets.

Contribution

The paper presents GenBEE, a novel event structure-aware generative model that incorporates structural prompts and label semantics to improve biomedical event extraction performance.

Findings

Achieved state-of-the-art results on MLEE and GE11 datasets.

Performed competitively on PHEE dataset compared to classification models.

Effectively models nested and overlapping events in biomedical texts.

Abstract

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 recent advancements in large language models (LLMs), generation-based models that cast event extraction as a sequence generation problem have attracted attention in the NLP research community. However, current generative models often overlook cross-instance information in complex event structures, such as nested and overlapping events, which constitute over 20% of events in benchmark datasets. In this paper, we propose GenBEE, an event structure-aware generative model that captures complex event structures in biomedical text for biomedical event extraction. GenBEE constructs event prompts that distill knowledge from LLMs to incorporate both label semantics and argument dependency relationships. In addition, GenBEE generates prefixes with event structural prompts to incorporate structural features to improve the model's overall performance. We have evaluated the proposed GenBEE model on three widely used BEE 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.