Multiple Optimization with Retrieval-Augmented Generation and Fine-Grained Denoising for Biomedical Event Causal Relation Extraction

Jie Zhao, Lishuang Li, Wanting Ning, Jing Hao, Yuxiao Fei, Jiayi Huang

Abstract: Biomedical Event Causal Relation Extraction (BECRE) aims to identify event causal relations in biomedical literature. Current methods emphasize sample data optimization. First, although data augmentation tackles the low-resource issue, it only augments data from the lexical level. Second, integrating external knowledge enriches data diversity, yet fails to select targeted knowledge. Third, fine-grained sample denoising is often overlooked. To resolve the aforementioned issues, we introduce Multiple Optimization with Retrieval-Augmented Generation and Fine-Grained Denoising (MoRAG-FD) framework. It augments data from multiple perspectives, which mainly include Retrieval-Augmented Generation for semantic enrichment and targeted selection of external knowledge. Additionally, we accomplish fine-grained data denoising by assigning near-zero weights to entity pairs without syntactic dependencies in events, rather than simply filtering them based on word index distance. Experimental results show our framework outperforms current methods on Hahn-Powell’s dataset and BioCause dataset.