@inproceedings{zhang-etal-2025-multi,
    title = "Multi-Domain {A}ncient {C}hinese Named Entity Recognition Based on Attention-Enhanced Pre-trained Language Model",
    author = "Zhang, Qi  and
      Duan, Zhiya  and
      Ma, Shijie  and
      Liu, Shengyu  and
      Yuan, Zibo  and
      Ma, RuiMin",
    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-1.32/",
    pages = "237--241",
    ISBN = "979-8-89176-235-0",
    abstract = "Recent advancements in digital humanities have intensified the demand for intelligent processing of ancient Chinese texts, particularly across specialized domains such as historical records and ancient medical literature. Among related research areas, Named Entity Recognition (NER) plays a crucial role, serving as the foundation for knowledge graph construction and deeper humanities computing studies. In this paper, we introduce a architecture specifically designed for multi-domain ancient Chinese NER tasks based on a pre-trained language model (PLM). Building upon the GujiRoberta backbone, we propose the GujiRoberta-BiLSTM-Attention-CRF model. Experimental results on three distinct domain-specific datasets demonstrate that our approach significantly outperforms the official baselines across all three datasets, highlighting the particular effectiveness of integrating an attention mechanism within our architecture."
}