Semantic chunking that respects entity and relationship boundaries significantly outperforms fixed-size chunking for biomedical RAG, especially when relation cues are explicit in the text.
This paper improves biomedical information extraction in RAG systems by replacing fixed-size text chunking with a configurable semantic chunking framework. The approach preserves important entities and relationships by using trigger-centered chunking and hierarchical relation resolution, achieving 8.4 F1 points improvement on biomedical relation extraction benchmarks.