Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework

• URL: http://arxiv.org/abs/2109.05357v1
• Date: Sat, 11 Sep 2021 19:52:09 GMT
• Title: Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
• Authors: Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao
• Abstract summary: We propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes. 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.
• Score: 23.501276952950366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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