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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
EditorsGuihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1241-1246
Number of pages6
ISBN (Electronic)9798350307887
ISBN (Print)9798350307894
DOIs
Publication statusPublished - 2023
EventIEEE International Conference on Data Mining 2023 - Shanghai, China
Duration: 1 Dec 20234 Dec 2023
Conference number: 23rd
https://ieeexplore.ieee.org/xpl/conhome/10415628/proceeding (Proceedings)
https://www.cloud-conf.net/icdm2023/ (Website)

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1550-4786
ISSN (Electronic)2374-8486

Conference

ConferenceIEEE International Conference on Data Mining 2023
Abbreviated titleICDM 2023
Country/TerritoryChina
CityShanghai
Period1/12/234/12/23
Internet address

Keywords

  • AUC
  • Low-Budget
  • NER
  • NLP
  • One-vs-All

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