Named entity recognition in real-world applications suffers from the
diversity of entity types, the emergence of new entity types, and the lack of
high-quality annotations. To address the above problems, this paper proposes an
in-context learning-based NER approach, which can effectively inject in-context
NER ability into PLMs and recognize entities of novel types on-the-fly using
only a few demonstrative instances. Specifically, we model PLMs as a
meta-function $\mathcal{ \lambda_ {\text{instruction, demonstrations, text}}.
M}$, and a new entity extractor can be implicitly constructed by applying new
instruction and demonstrations to PLMs, i.e., $\mathcal{ (\lambda . M)
}$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be
a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the
above in-context NER ability into PLMs, we propose a meta-function pre-training
algorithm, which pre-trains PLMs by comparing the (instruction,
demonstration)-initialized extractor with a surrogate golden extractor.
Experimental results on 4 few-shot NER datasets show that our method can
effectively inject in-context NER ability into PLMs and significantly
outperforms the PLMs+fine-tuning counterparts.

本研究提出了一种基于上下文学习的命名实体识别方法，通过 PLMs 中的元函数，仅使用少量的示例即可识别新类型的实体，并使用元函数预训练算法将上下文 NER 能力注入 PLMs，实验证明该方法可以显著优于 PLMs + 微调对照组。