# Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?

arXiv: https://arxiv.org/abs/2311.04918 · HTML: https://mlsanity.com/paper/2311.04918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [OVANet: One-vs-All Network for Universal Domain Adaptation](https://mlsanity.com/paper/2104.03344/) (2021)
- [Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification](https://mlsanity.com/paper/2012.03173/) (2020)
- [One-Pass AUC Optimization](https://mlsanity.com/paper/1305.1363/) (2013)
- [MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER](https://mlsanity.com/paper/2108.13655/) (2021)
- [Learning with Multiclass AUC: Theory and Algorithms](https://mlsanity.com/paper/2107.13171/) (2021)
- [Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks](https://mlsanity.com/paper/2007.05134/) (2020)
- [Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation](https://mlsanity.com/paper/2108.11703/) (2021)
- [One-vs-Rest Network-based Deep Probability Model for Open Set Recognition](https://mlsanity.com/paper/2004.08067/) (2020)
- [Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios](https://mlsanity.com/paper/2211.05980/) (2022)
- [AUC Maximization for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2212.04800/) (2022)

## Built on by (most cited first, 2 of 2)

- [How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses](https://mlsanity.com/paper/2405.00291/) (2024)
- [Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification](https://mlsanity.com/paper/2401.07395/) (2024)

## Abstract (arXiv)

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.