DCA-Net: Graph-based Dependent Sampling and Dynamic Context Association for Biomedical Trigger Detection

Biomedical event trigger detection remains a foundational yet challenging task due to the pronounced long-tail distribution of trigger types, where rare event classes suffer from severe underrepresentation. To address this class imbalance, we propose DCA-Net, a novel framework integrating semantic Markov Chain Monte Carlo (MCMC) sampling with dynamic context association (DCA) mechanisms. Our approach first constructs a trigger transition graph to perform semantic MCMC sampling on neighboring triggers, enabling large language models (LLMs) to generate class-discriminative synthetic instances through semantic guidance. The framework further incorporates a gated attention-based DCA module that dynamically captures multi-granularity trigger-context dependencies using adaptive receptive fields. Complemented by a class-weighted focal loss emphasizing hard-to-learn rare triggers, DCA-Net achieves state-of-the-art performance across MLEE, BioNLP2011-Genia, and BioNLP2013-Genia benchmarks with F1-scores of 90.63 %, 87.52 %, and 75.39 % respectively, outperforming existing methods by up to 8.6 %. Ablation studies systematically validate the synergistic benefits of our graph-guided augmentation and adaptive context modeling components.