Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

Yu Meng, Yunyi Zhang, Jiaxin Huang, Xuan Wang, Yu Zhang, Heng Ji, Jiawei Han


Abstract
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. The biggest challenge of distantly-supervised NER is that the distant supervision may induce incomplete and noisy labels, rendering the straightforward application of supervised learning ineffective. In this paper, we propose (1) a noise-robust learning scheme comprised of a new loss function and a noisy label removal step, for training NER models on distantly-labeled data, and (2) a self-training method that uses contextualized augmentations created by pre-trained language models to improve the generalization ability of the NER model. On three benchmark datasets, our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.
Anthology ID:
2021.emnlp-main.810
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10367–10378
Language:
URL:
https://aclanthology.org/2021.emnlp-main.810
DOI:
10.18653/v1/2021.emnlp-main.810
Bibkey:
Cite (ACL):
Yu Meng, Yunyi Zhang, Jiaxin Huang, Xuan Wang, Yu Zhang, Heng Ji, and Jiawei Han. 2021. Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10367–10378, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (Meng et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.810.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.810.mp4
Code
 yumeng5/roster