Demonstration-based learning for few-shot biomedical named entity
  recognition under machine reading comprehension
Leilei Su Jian Chen Yifan Peng Cong Sun
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.