@inproceedings{wang-etal-2021-learning-language-description,
title = "Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework",
author = "Wang, Yaqing and
Chu, Haoda and
Zhang, Chao and
Gao, Jing",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.139",
doi = "10.18653/v1/2021.findings-emnlp.139",
pages = "1618--1630",
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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<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.</abstract>
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%0 Conference Proceedings
%T Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
%A Wang, Yaqing
%A Chu, Haoda
%A Zhang, Chao
%A Gao, Jing
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F wang-etal-2021-learning-language-description
%X 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.
%R 10.18653/v1/2021.findings-emnlp.139
%U https://aclanthology.org/2021.findings-emnlp.139
%U https://doi.org/10.18653/v1/2021.findings-emnlp.139
%P 1618-1630
Markdown (Informal)
[Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework](https://aclanthology.org/2021.findings-emnlp.139) (Wang et al., Findings 2021)
ACL