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

arXiv: https://arxiv.org/abs/2211.05980 · HTML: https://mlsanity.com/paper/2211.05980/

## 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.

## Builds on (most cited first, 18 of 18)

- [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)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Task2Vec: Task Embedding for Meta-Learning](https://mlsanity.com/paper/1902.03545/) (2019)
- [Domain Generalization by Marginal Transfer Learning](https://mlsanity.com/paper/1711.07910/) (2017)
- [Feature-Critic Networks for Heterogeneous Domain Generalization](https://mlsanity.com/paper/1901.11448/) (2019)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [Adaptive Task Sampling for Meta-Learning](https://mlsanity.com/paper/2007.08735/) (2020)
- [Meta-learning with an Adaptive Task Scheduler](https://mlsanity.com/paper/2110.14057/) (2021)
- [Probabilistic Active Meta-Learning](https://mlsanity.com/paper/2007.08949/) (2020)
- [Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning](https://mlsanity.com/paper/2105.14099/) (2021)
- [Clinical Concept Extraction with Contextual Word Embedding](https://mlsanity.com/paper/1810.10566/) (2018)
- [Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification](https://mlsanity.com/paper/2007.06240/) (2020)
- [Probabilistic task modelling for meta-learning](https://mlsanity.com/paper/2106.04802/) (2021)

## Built on by (most cited first, 6 of 6)

- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [AUC Maximization for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2212.04800/) (2022)
- [Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?](https://mlsanity.com/paper/2311.04918/) (2023)
- [Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences](https://mlsanity.com/paper/2401.10472/) (2024)
- [Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction](https://mlsanity.com/paper/2401.10189/) (2024)
- [Extracting Biomedical Entities from Noisy Audio Transcripts](https://mlsanity.com/paper/2403.17363/) (2024)

## 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