@inproceedings{39eb5bacf7dd441d8f9ed10dc56d5e38,
title = "Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?",
abstract = "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.",
keywords = "AUC, Low-Budget, NER, NLP, One-vs-All",
author = "Nguyen, \{Ngoc Dang\} and Wei Tan and Lan Du and Wray Buntine and Richard Beare and Changyou Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining, ICDM 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDM58522.2023.00155",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1241--1246",
editor = "Guihai Chen and Latifur Khan and Xiaofeng Gao and Meikang Qiu and Witold Pedrycz and Xindong Wu",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023",
address = "United States",
}