AUC Maximization for Low-Resource Named Entity Recognition

Understand

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model.

• Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples.
• But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions.
• Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score.