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

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.