GAT-MG-ECF: A Dependency Graph-Attention Extension of Multi-Granularity Entity–Context Fusion for Drug–Drug Interaction Extraction

Harmful drug–drug interactions (DDIs) are a common, preventable cause of hospitalizations, adverse drug events, and mistaken treatment decisions, and extracting them from the biomedical literature supports online pharmacovigilance and clinical decision support. Prior extractors fuse several views of a sentence from a pre-trained encoder, but they pool every view from contiguous token spans and use syntactic dependency structure only implicitly. We propose GAT-MG-ECF(Graph-Attention Multi-Granularity Entity–Context Fusion): it adds a fourth, dependency-graph view—a graph attention network (GAT) over the sentence’s dependency parse—to the entity, context-bridge, and global views of its three-view predecessor, MG-ECF. The graph is built from the sentence alone, so the model needs no external knowledge base. On DDI-2013, under the official four-class protocol (three seeds), GAT-MG-ECF with BiomedBERT reaches micro-F1 0.9092±0.0039—to the best of our knowledge, the highest reported under this protocol; it surpasses the three-view MG-ECF (0.905) and outperforms the strongest knowledge-graph-enhanced system, HKG-DDIE (0.854), by 5.5 percentage points. A second backbone, BioLinkBERT, also exceeds the prior best (0.9076), and the architecture transfers to ChemProt without re-tuning (micro-F1 0.9064). The graph view’s own contribution is modest but consistent across both backbones, adding complementary rather than redundant signal. Explicit syntactic structure is a useful, low-cost addition to strong contextual encoders for biomedical relation classification.