2023/05/18 by Chen, Jiawei, Lu, Yaojie, Lin, Hongyu +7 · 1 citation
#Computation and Language (cs.CL) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2305.11038
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 λ_ instruction, demonstrations, text. M, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i.e., (λ. M) (instruction, demonstrations) → F where F will be a new entity extractor, i.e., F: text → 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.