@inproceedings{lu-lei-2025-construction,
    title = "Construction of {NER} Model in {A}ncient {C}hinese: Solution of {E}va{H}an 2025 Challenge",
    author = "Lu, Yi  and
      Lei, Minyi",
    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-1.20/",
    pages = "165--169",
    ISBN = "979-8-89176-235-0",
    abstract = "This paper introduces the system submit-ted for EvaHan 2025, focusing on the Named Entity Recognition (NER) task for ancient Chinese texts. Our solution is built upon two specified pre-trained BERT models, namely GujiRoBERTa{\_}jian{\_}fan and GujiRoBERTa{\_}fan, and further en-hanced by a deep BiLSTM network with a Conditional Random Field (CRF) decod-ing layer. Extensive experiments on three test dataset splits demonstrate that our system{'}s performance, 84.58{\%} F1 in the closed-modality track and 82.78{\%} F1 in the open-modality track, significantly out-performs the official baseline, achieving no-table improvements in F1 score."
}