Effective Multi-Task Learning for Biomedical Named Entity Recognition
Biomedical named entity recognition (BM-NER) faces challenges including terminological complexity, pervasive nested entity structures, and inconsistent annotations across datasets. To address these, we propose SRU-NER: a framework featuring a Slot-based Recurrent Unit (SRU) that explicitly models nested entities; multi-task learning to jointly train on heterogeneous biomedical and general-domain NER datasets; and a novel dynamic loss adjustment mechanism that automatically suppresses supervision signals for unannotated entity types—effectively mitigating negative transfer induced by annotation discrepancies. Crucially, SRU-NER enables unified modeling of both biomedical and general-domain NER. Experimental results demonstrate state-of-the-art or competitive performance across multiple benchmark datasets. Cross-domain evaluation and human assessment further validate its strong generalization capability and practical effectiveness.