  This paper presents a comprehensive study to efficiently build named entity
recognition (NER) systems when a small number of in-domain labeled data is
available. Based upon recent Transformer-based self-supervised pre-trained
language models (PLMs), we investigate three orthogonal schemes to improve the
model generalization ability for few-shot settings: (1) meta-learning to
construct prototypes for different entity types, (2) supervised pre-training on
noisy web data to extract entity-related generic representations and (3)
self-training to leverage unlabeled in-domain data. Different combinations of
these schemes are also considered. We perform extensive empirical comparisons
on 10 public NER datasets with various proportions of labeled data, suggesting
useful insights for future research. Our experiments show that (i) in the
few-shot learning setting, the proposed NER schemes significantly improve or
outperform the commonly used baseline, a PLM-based linear classifier fine-tuned
on domain labels; (ii) We create new state-of-the-art results on both few-shot
and training-free settings compared with existing methods. We will release our
code and pre-trained models for reproducible research.
