Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge

Yi Lu; Minyi Lei

doi:10.18653/v1/2025.alp-1.20

Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge

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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.

Anthology ID:: 2025.alp-1.20
Volume:: Proceedings of the Second Workshop on Ancient Language Processing
Month:: May
Year:: 2025
Address:: The Albuquerque Convention Center, Laguna
Editors:: Adam Anderson, Shai Gordin, Bin Li, Yudong Liu, Marco C. Passarotti, Rachele Sprugnoli
Venues:: ALP | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 165–169
Language:
URL:: https://aclanthology.org/2025.alp-1.20/
DOI:: 10.18653/v1/2025.alp-1.20
Bibkey:
Cite (ACL):: Yi Lu and Minyi Lei. 2025. Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge. In Proceedings of the Second Workshop on Ancient Language Processing, pages 165–169, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.
Cite (Informal):: Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge (Lu & Lei, ALP 2025)
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PDF:: https://aclanthology.org/2025.alp-1.20.pdf

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@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://aclanthology.org/2025.alp-1.20/",
    doi = "10.18653/v1/2025.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."
}

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%0 Conference Proceedings
%T Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge
%A Lu, Yi
%A Lei, Minyi
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%Y Sprugnoli, Rachele
%S Proceedings of the Second Workshop on Ancient Language Processing
%D 2025
%8 May
%I Association for Computational Linguistics
%C The Albuquerque Convention Center, Laguna
%@ 979-8-89176-235-0
%F lu-lei-2025-construction
%X 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.
%R 10.18653/v1/2025.alp-1.20
%U https://aclanthology.org/2025.alp-1.20/
%U https://doi.org/10.18653/v1/2025.alp-1.20
%P 165-169

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Markdown (Informal)

[Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge](https://aclanthology.org/2025.alp-1.20/) (Lu & Lei, ALP 2025)

Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge (Lu & Lei, ALP 2025)

ACL

Yi Lu and Minyi Lei. 2025. Construction of NER Model in Ancient Chinese: Solution of EvaHan 2025 Challenge. In Proceedings of the Second Workshop on Ancient Language Processing, pages 165–169, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.