MG-ECF: Multi-Granularity Entity-Context Fusion for Drug–Drug Interaction Extraction

Adverse drug–drug interactions (DDIs) are a leading cause of preventable medication-related harm, and their automatic detection from the biomedical literature is critical for pharmacovigilance and clinical decision support. Most existing systems derive relation representations from a single source, under-using the rich contextual structure of DDI sentences. We propose MG-ECF (Multi-Granularity Entity-Context Fusion), a relation-classification architecture that extracts three complementary views from a shared biomedical encoder—entity-level, inter-entity contextual, and global sentence representations—which are adaptively combined through a temperature-scaled gating mechanism regularized by view-dropout. MG-ECF was evaluated on the DDI-2013 benchmark under the official shared-task protocol, with multi-seed experiments on BioBERT and BiomedBERT backbones and a focal-loss objective to address severe class imbalance. MG-ECF achieves a mean micro-F1 of 90.55% with BiomedBERT and 88.8% with BioBERT, an absolute improvement of 2.7 F1 points over the strongest previously reported PLM-based system (BioMCL-DDI, 87.8%). Systematic component analyses confirm the contribution of each representational view, demonstrating the effectiveness of multi-granularity fusion for DDI classification and its potential as a research-stage building block for Internet-based pharmacovigilance platforms and networked clinical decision support systems, pending real-world clinical validation.