@inproceedings{li-etal-2025-overview,
    title = "Overview of {E}va{H}an2025: The First International Evaluation on {A}ncient {C}hinese Named Entity Recognition",
    author = "Li, Bin  and
      Chang, Bolin  and
      Liu, Ruilin  and
      Zhao, Xue  and
      Shen, Si  and
      Liu, Lihong  and
      Zhu, Yan  and
      Xu, Zhixing  and
      Qu, Weiguang  and
      Wang, Dongbo",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.alp-1.19/",
    pages = "156--164",
    ISBN = "979-8-89176-235-0",
    abstract = "Ancient Chinese books have great values in history and cultural studies. Named en-tities like person, location, time are cru-cial elements, thus automatic Named En-tity Recognition (NER) is considered a ba-sic task in ancient Chinese text processing. This paper introduces EvaHan2025, the first international ancient Chinese Named Entity Recognition bake-off. The evalua-tion introduces a rigorous benchmark for assessing NER performance across histori-cal and medical texts, covering 12 named entity types. A total of 13 teams par-ticipated in the competition, submitting 77 system runs. In the closed modality, where participants were restricted to us-ing only the training data, the highest F1 scores reached 85.04{\%} on TestA and 90.28{\%} on TestB, both derived from his-torical texts, while performance on medi-cal texts (TestC) reached 84.49{\%}. The re-sults indicate that text genre significantly impacts model performance, with histori-cal texts generally yielding higher scores. Additionally, the intrinsic characteristics of named entities also influence recogni-tion performance. These findings high-light the challenges and opportunities in ancient Chinese NER and underscore the importance of domain adaptation and en-tity type diversity in future research."
}