@inproceedings{liu-etal-2023-addressing,
    title = "Addressing {NER} Annotation Noises with Uncertainty-Guided Tree-Structured {CRF}s",
    author = "Liu, Jian  and
      Liu, Weichang  and
      Chen, Yufeng  and
      Xu, Jinan  and
      Zhao, Zhe",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.872/",
    doi = "10.18653/v1/2023.emnlp-main.872",
    pages = "14112--14123",
    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."
}