mlsanityData to 14 Oct 2026

Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios

arXiv 2211.05980 ↗First posted 11 Nov 2022cs.CL cs.LGcited by 6

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Abstract (arXiv)

Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatisfactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-the-art (SOTA) MetaNER model

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 11 Nov 2022.

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18 of 36 references.

References · 18

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6 more
  1. 2021Meta-learning with an Adaptive Task Schedulercited by 27
  2. 2020Probabilistic Active Meta-Learningcited by 23
  3. 2021Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learningcited by 21
  4. 2018Clinical Concept Extraction with Contextual Word Embeddingcited by 12
  5. 2020Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classificationcited by 6
  6. 2021Probabilistic task modelling for meta-learningcited by 4

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Citing papers per year: 2022: 1; 2023: 1; 2024: 4.

6 papers.

  1. 2024From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?cited by 11
  2. 2022AUC Maximization for Low-Resource Named Entity Recognitioncited by 6
  3. 2023Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?cited by 2
  4. 2024Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciencescited by 2
  5. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1
  6. 2024Extracting Biomedical Entities from Noisy Audio Transcriptscited by 1