Configurable Semantic Chunking for Biomedical Information Extraction in Retrieval-Augmented Generation

TL;DR

The paper introduces a configurable semantic chunking framework that enhances biomedical information extraction in retrieval-augmented generation by using dynamic, context-aware chunking strategies. It replaces fixed-size chunking in BioMedRAG with methods like entity-preserving windows and trigger-centered extraction, improving performance on relation extraction tasks while maintaining compatibility with existing models and evaluation setups.

Why It Matters

This approach enables more accurate and interpretable biomedical text processing by adapting chunking to semantic structure, which is crucial for downstream tasks like drug interaction detection and adverse event classification. The modular design allows easy experimentation and deployment across diverse biomedical NLP pipelines.

Key Findings

• The hybrid semantic chunking configuration achieves 82.6% F1 on GM-CIHT, a significant improvement over the fixed-size baseline's 74.2% F1.
• Semantic chunking outperforms fixed-size chunking on datasets with explicit relation cues like GM-CIHT and DDI, but fixed chunking remains competitive on dense biochemical extraction and binary classification tasks such as ChemProt and ADE.