Effective Multi-Task Learning for Biomedical Named Entity Recognition

• URL: http://arxiv.org/abs/2507.18542v1
• Date: Thu, 24 Jul 2025 16:08:15 GMT
• Title: Effective Multi-Task Learning for Biomedical Named Entity Recognition
• Authors: João Ruano, Gonçalo M. Correia, Leonor Barreiros, Afonso Mendes,
• Abstract summary: This paper introduces SRU-NER, a novel approach designed to handle nested named entities while integrating multiple datasets.<n>SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset.
• Score: 1.53387176937131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.

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