Few-shot Named Entity Recognition with Supported and Dependent Label Representations

Abstract

We explore the problem of few-shot named entity recognition (NER) by introducing two ideas to improve label representations. Recently, the use of token representations with a distance metric has been shown to be effective in few-shot NER, and we take an approach to use label representations along with token representations. Firstly, we add support examples to a label name (e.g., "person; example: Federic Krupp, Gao, Honecker, Bush, Deverow") when obtaining a label representation. Secondly, we estimate a transition score among labels with a bilinear function among label representations. The proposed approach is evaluated on 4 open few-shot NER datasets and we found that the approach can improve the performance of one-stage few-shot NER.