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.
Original language | English |
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Title of host publication | Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023 |
Editors | Guihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1241-1246 |
Number of pages | 6 |
ISBN (Electronic) | 9798350307887 |
ISBN (Print) | 9798350307894 |
DOIs | |
Publication status | Published - 2023 |
Event | IEEE International Conference on Data Mining 2023 - Shanghai, China Duration: 1 Dec 2023 → 4 Dec 2023 Conference number: 23rd https://ieeexplore.ieee.org/xpl/conhome/10415628/proceeding (Proceedings) https://www.cloud-conf.net/icdm2023/ (Website) |
Publication series
Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISSN (Print) | 1550-4786 |
ISSN (Electronic) | 2374-8486 |
Conference
Conference | IEEE International Conference on Data Mining 2023 |
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Abbreviated title | ICDM 2023 |
Country/Territory | China |
City | Shanghai |
Period | 1/12/23 → 4/12/23 |
Internet address |
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Keywords
- AUC
- Low-Budget
- NER
- NLP
- One-vs-All