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    <abstract>Real-world named entity recognition (NER) datasets are notorious for their noisy nature, attributed to annotation errors, inconsistencies, and subjective interpretations. Such noises present a substantial challenge for traditional supervised learning methods. In this paper, we present a new and unified approach to tackle annotation noises for NER. Our method considers NER as a constituency tree parsing problem, utilizing a tree-structured Conditional Random Fields (CRFs) with uncertainty evaluation for integration. Through extensive experiments conducted on four real-world datasets, we demonstrate the effectiveness of our model in addressing both partial and incorrect annotation errors. Remarkably, our model exhibits superb performance even in extreme scenarios with 90% annotation noise.</abstract>
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%0 Conference Proceedings
%T Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs
%A Liu, Jian
%A Liu, Weichang
%A Chen, Yufeng
%A Xu, Jinan
%A Zhao, Zhe
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-addressing
%X Real-world named entity recognition (NER) datasets are notorious for their noisy nature, attributed to annotation errors, inconsistencies, and subjective interpretations. Such noises present a substantial challenge for traditional supervised learning methods. In this paper, we present a new and unified approach to tackle annotation noises for NER. Our method considers NER as a constituency tree parsing problem, utilizing a tree-structured Conditional Random Fields (CRFs) with uncertainty evaluation for integration. Through extensive experiments conducted on four real-world datasets, we demonstrate the effectiveness of our model in addressing both partial and incorrect annotation errors. Remarkably, our model exhibits superb performance even in extreme scenarios with 90% annotation noise.
%R 10.18653/v1/2023.emnlp-main.872
%U https://aclanthology.org/2023.emnlp-main.872
%U https://doi.org/10.18653/v1/2023.emnlp-main.872
%P 14112-14123

Markdown (Informal)

[Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs](https://aclanthology.org/2023.emnlp-main.872) (Liu et al., EMNLP 2023)

• Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs (Liu et al., EMNLP 2023)

ACL

• Jian Liu, Weichang Liu, Yufeng Chen, Jinan Xu, and Zhe Zhao. 2023. Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14112–14123, Singapore. Association for Computational Linguistics.