@inproceedings{brisson-etal-2025-named,
    title = "Named Entity Recognition in Context: {E}dit{\_}{D}unhuang team Technical Report for Evahan2025 {NER} Competition",
    author = "Brisson, Colin  and
      Kahfy, Ayoub  and
      Bui, Marc  and
      Constant, Fr{\'e}d{\'e}ric",
    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-1.22/",
    pages = "176--181",
    ISBN = "979-8-89176-235-0",
    abstract = "We present the Named Entity Recognition sys-tem developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach in-tegrates three core components: (1) Pindola, a modern transformer-based bidirectional en-coder pretrained on a large corpus of Classi-cal Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points."
}