@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://aclanthology.org/2025.alp-1.24/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T GRoWE: A GujiRoBERTa-Enhanced Approach to Ancient Chinese NER via Word-Word Relation Classification and Model Ensembling
%A Xia, Tian
%A Wang, Yilin
%A Wang, Xinkai
%A Yang, Yahe
%A Zhao, Qun
%A Yang, Menghui
%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 xia-etal-2025-growe
%X 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.
%R 10.18653/v1/2025.alp-1.24
%U https://aclanthology.org/2025.alp-1.24/
%U https://doi.org/10.18653/v1/2025.alp-1.24
%P 187-191
Markdown (Informal)
[GRoWE: A GujiRoBERTa-Enhanced Approach to Ancient Chinese NER via Word-Word Relation Classification and Model Ensembling](https://aclanthology.org/2025.alp-1.24/) (Xia et al., ALP 2025)
ACL