Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization
  Help?
Ngoc Dang Nguyen Wei Tan Lan Du Wray Buntine Richard Beare Changyou Chen
Named entity recognition (NER), a task that identifies and categorizes named
entities such as persons or organizations from text, is traditionally framed as
a multi-class classification problem. However, this approach often overlooks
the issues of imbalanced label distributions, particularly in low-resource
settings, which is common in certain NER contexts, like biomedical NER
(bioNER). To address these issues, we propose an innovative reformulation of
the multi-class problem as a one-vs-all (OVA) learning problem and introduce a
loss function based on the area under the receiver operating characteristic
curve (AUC). To enhance the efficiency of our OVA-based approach, we propose
two training strategies: one groups labels with similar linguistic
characteristics, and another employs meta-learning. The superiority of our
approach is confirmed by its performance, which surpasses traditional NER
learning in varying NER settings.