@inproceedings{xia-etal-2025-growe,
    title = "{GR}o{WE}: A {G}uji{R}o{BERT}a-Enhanced Approach to {A}ncient {C}hinese {NER} via Word-Word Relation Classification and Model Ensembling",
    author = "Xia, Tian  and
      Wang, Yilin  and
      Wang, Xinkai  and
      Yang, Yahe  and
      Zhao, Qun  and
      Yang, Menghui",
    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/landing_page/2025.alp-1.24/",
    pages = "187--191",
    ISBN = "979-8-89176-235-0",
    abstract = "Named entity recognition is a fundamental task in ancient Chinese text analysis.Based on the pre-trained language model of ancient Chinese texts, this paper proposes a new named entity recognition method GRoWE. It uses the ancient Chinese texts pre-trained language model GujiRoBERTa as the base model, and the wordword relation prediction model is superposed upon the base model to construct a superposition model. Then ensemble strategies are used to multiple superposition models. On the EvaHan 2025 public test set, the F1 value of the proposed method reaches 86.79{\%}, which is 6.18{\%} higher than that of the mainstream BERT{\_}LSTM{\_}CRF baseline model, indicating that the model architecture and ensemble strategy play an important role in improving the recognition effect of naming entities in ancient Chinese texts."
}