Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning. By redefining biomedical named entity recognition (BioNER) as a machine reading comprehension (MRC) problem, we propose a demonstration-based learning method to address few-shot BioNER, which involves constructing appropriate task demonstrations. In assessing our proposed method, we compared the proposed method with existing advanced methods using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical, BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models' efficacy by reporting F1 scores from both the 25-shot and 50-shot learning experiments. In 25-shot learning, we observed 1.1% improvements in the average F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%, 50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further improved the average F1 scores by 1.0% compared to the baseline method, reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We reported that in the realm of few-shot learning BioNER, MRC-based language models are much more proficient in recognizing biomedical entities compared to the sequence labeling approach. Furthermore, our MRC-language models can compete successfully with fully-supervised learning methodologies that rely heavily on the availability of abundant annotated data. These results highlight possible pathways for future advancements in few-shot BioNER methodologies.

翻译：尽管深度学习技术已取得显著成就，但其通常高度依赖大量人工标注数据，且在少样本场景下表现欠佳。本研究旨在设计一种策略，以提升模型在少样本学习场景中识别生物医学实体的能力。通过将生物医学命名实体识别（BioNER）重新定义为机器阅读理解（MRC）问题，我们提出了一种基于演示的少样本学习方法，该方法涉及构建合适的任务演示。为评估所提方法，我们使用六个基准数据集（包括BC4CHEMD、BC5CDR-Chemical、BC5CDR-Disease、NCBI-Disease、BC2GM和JNLPBA），将其与现有先进方法进行了比较。通过报告25-shot和50-shot学习实验的F1分数，我们检验了模型的有效性。在25-shot学习中，与基线方法相比，六个数据集上的平均F1分数分别提升了1.1%，达到61.7%、84.1%、69.1%、70.1%、50.6%和59.9%。在50-shot学习中，平均F1分数进一步较基线方法提升了1.0%，分别达到73.1%、86.8%、76.1%、75.6%、61.7%和65.4%。我们报告指出，在少样本学习BioNER领域，基于MRC的语言模型在识别生物医学实体方面远优于序列标注方法。此外，我们的MRC语言模型能够与高度依赖大量标注数据的全监督学习方法成功竞争。这些结果突显了未来少样本BioNER方法发展的潜在路径。