@inproceedings{zhang-etal-2025-dynclean,
title = "{D}yn{C}lean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition",
author = "Zhang, Qi and
Pan, Huitong and
Chen, Zhijia and
Latecki, Longin Jan and
Caragea, Cornelia and
Dragut, Eduard",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.137/",
doi = "10.18653/v1/2025.findings-naacl.137",
pages = "2540--2556",
ISBN = "979-8-89176-195-7",
abstract = "Distantly Supervised Named Entity Recognition (DS-NER) has attracted attention due to its scalability and ability to automatically generate labeled data. However, distant annotation introduces many mislabeled instances, limiting its performance. Most of the existing work attempt to solve this problem by developing intricate models to learn from the noisy labels. An alternative approach is to attempt to clean the labeled data, thus increasing the quality of distant labels. This approach has received little attention for NER. In this paper, we propose a training dynamics-based label cleaning approach, which leverages the behavior of a model as training progresses to characterize the distantly annotated samples. We also introduce an automatic threshold estimation strategy to locate the errors in distant labels. Extensive experimental results demonstrate that: (1) models trained on our cleaned DS-NER datasets, which were refined by directly removing identified erroneous annotations, achieve significant improvements in F1-score, ranging from 3.18{\%} to 8.95{\%}; and (2) our method outperforms numerous advanced DS-NER approaches across four datasets."
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<abstract>Distantly Supervised Named Entity Recognition (DS-NER) has attracted attention due to its scalability and ability to automatically generate labeled data. However, distant annotation introduces many mislabeled instances, limiting its performance. Most of the existing work attempt to solve this problem by developing intricate models to learn from the noisy labels. An alternative approach is to attempt to clean the labeled data, thus increasing the quality of distant labels. This approach has received little attention for NER. In this paper, we propose a training dynamics-based label cleaning approach, which leverages the behavior of a model as training progresses to characterize the distantly annotated samples. We also introduce an automatic threshold estimation strategy to locate the errors in distant labels. Extensive experimental results demonstrate that: (1) models trained on our cleaned DS-NER datasets, which were refined by directly removing identified erroneous annotations, achieve significant improvements in F1-score, ranging from 3.18% to 8.95%; and (2) our method outperforms numerous advanced DS-NER approaches across four datasets.</abstract>
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%0 Conference Proceedings
%T DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition
%A Zhang, Qi
%A Pan, Huitong
%A Chen, Zhijia
%A Latecki, Longin Jan
%A Caragea, Cornelia
%A Dragut, Eduard
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F zhang-etal-2025-dynclean
%X Distantly Supervised Named Entity Recognition (DS-NER) has attracted attention due to its scalability and ability to automatically generate labeled data. However, distant annotation introduces many mislabeled instances, limiting its performance. Most of the existing work attempt to solve this problem by developing intricate models to learn from the noisy labels. An alternative approach is to attempt to clean the labeled data, thus increasing the quality of distant labels. This approach has received little attention for NER. In this paper, we propose a training dynamics-based label cleaning approach, which leverages the behavior of a model as training progresses to characterize the distantly annotated samples. We also introduce an automatic threshold estimation strategy to locate the errors in distant labels. Extensive experimental results demonstrate that: (1) models trained on our cleaned DS-NER datasets, which were refined by directly removing identified erroneous annotations, achieve significant improvements in F1-score, ranging from 3.18% to 8.95%; and (2) our method outperforms numerous advanced DS-NER approaches across four datasets.
%R 10.18653/v1/2025.findings-naacl.137
%U https://aclanthology.org/2025.findings-naacl.137/
%U https://doi.org/10.18653/v1/2025.findings-naacl.137
%P 2540-2556
Markdown (Informal)
[DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition](https://aclanthology.org/2025.findings-naacl.137/) (Zhang et al., Findings 2025)
ACL