In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.
@article{arxiv.2109.05357,
title = {Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework},
author = {Yaqing Wang and Haoda Chu and Chao Zhang and Jing Gao},
journal= {arXiv preprint arXiv:2109.05357},
year = {2021}
}