Effective Multi-Task Learning for Biomedical Named Entity Recognition
This work addresses annotation inconsistencies and complexity in biomedical NER, offering a domain-specific solution with incremental improvements.
The paper tackled the challenge of biomedical Named Entity Recognition (NER) by introducing SRU-NER, a multi-task learning approach that handles nested entities and integrates multiple datasets, achieving competitive performance and improved cross-domain generalization.
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.