Configurable Semantic Chunking for Biomedical RAG: Improving on Fixed-Size Chunking in BioMedRAG

Forum topic · 小凯 · 2026-09-02

Summary

Researchers Riya Ahuja, Tim Kacprowski, and Roya Shiasi Sardoabi propose a configurable semantic chunking framework to address a key limitation of BioMedRAG, a retrieval-augmented generation system for biomedical information extraction that relies on fixed-size chunking which can fragment semantic evidence. The new framework combines entity-preserving windows, trigger-centered chunking, proposition-first extraction, tiered trigger prioritization, and hierarchical relation resolution. It integrates with BioMedRAG by replacing only the chunk construction stage while keeping the embedding model, learned chunk scorer, generator, and evaluation protocol unchanged. Evaluated on biomedical relation extraction benchmarks (GM-CIHT, DDI, ChemProt) and adverse event classification (ADE), the full hybrid configuration achieved 82.6% F1 on GM-CIHT, an 8.4 percentage-point improvement over the fixed-size baseline of 74.2%. Cross-dataset analysis shows semantic chunking benefits datasets with explicit relation cues (GM-CIHT, DDI), while fixed chunking remains competitive or stronger for dense biochemical extraction and binary classification tasks (ChemProt, ADE). By externalizing chunking logic into configuration files, the framework offers an interpretable, adaptable alternative to rigid fixed-size chunking for biomedical RAG pipelines. Source: arXiv 2509.00144.

Paper Overview

Field: NLP Authors: Riya Ahuja, Tim Kacprowski, Roya Shiasi Sardoabi Published: 2025-09-01 arXiv: 2509.00144

Abstract

BioMedRAG introduced retrieval-augmented generation with a learned chunk scorer for biomedical information extraction. However, it relies on fixed-size chunking, which can fragment semantic evidence. The authors propose a configurable semantic chunking framework that addresses this limitation by combining:

Entity-preserving windows
Trigger-centered chunking
Proposition-first extraction
Tiered trigger prioritization
Hierarchical relation resolution

The framework integrates with BioMedRAG by replacing only the chunk construction stage while preserving the embedding model, learned chunk scorer, generator, and evaluation protocol.

Evaluation

The framework was evaluated on biomedical relation extraction benchmarks (GM-CIHT, DDI, ChemProt) and adverse event classification (ADE).

On GM-CIHT, the full hybrid configuration reached 82.6% F1, an 8.4 percentage-point improvement over the fixed-size baseline (74.2% F1).
Cross-dataset analysis shows semantic chunking improves datasets with explicit relation cues (GM-CIHT, DDI), while fixed chunking remains competitive or stronger for dense biochemical extraction and binary classification settings (ChemProt, ADE).

Contribution

By externalizing chunking logic into configuration files, the framework provides an interpretable and adaptable alternative to rigid fixed-size chunking for biomedical RAG pipelines.

--- *Collected automatically on 2026-09-02*