  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.
