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        <title>Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese</title>
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            <title>Proceedings of the Second Workshop on Ancient Language Processing</title>
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    <abstract>Named Entity Recognition (NER) is a fun-damental task in Natural Language Process-ing (NLP), particularly in the analysis of Chi-nese historical texts. In this work, we pro-pose an innovative NER model based on Gu-jiRoBERTa, incorporating Conditional Ran-dom Fields (CRF) and Long Short Term Mem-ory Network(LSTM) to enhance sequence la-beling performance. Our model is evaluated on three datasets from the EvaHan2025 competi-tion, demonstrating superior performance over the baseline model, SikuRoBERTa-BiLSTM-CRF. The proposed approach effectively cap-tures contextual dependencies and improves entity boundary recognition. Experimental re-sults show that our method achieves consistent improvements across almost all evaluation met-rics, highlighting its robustness and effective-ness in handling ancient Chinese texts.</abstract>
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%0 Conference Proceedings
%T Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese
%A Zhang, Yunmeng
%A Liu, Meiling
%A Tang, Hanqi
%A Lu, Shige
%A Xue, Lang
%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 zhang-etal-2025-simple
%X Named Entity Recognition (NER) is a fun-damental task in Natural Language Process-ing (NLP), particularly in the analysis of Chi-nese historical texts. In this work, we pro-pose an innovative NER model based on Gu-jiRoBERTa, incorporating Conditional Ran-dom Fields (CRF) and Long Short Term Mem-ory Network(LSTM) to enhance sequence la-beling performance. Our model is evaluated on three datasets from the EvaHan2025 competi-tion, demonstrating superior performance over the baseline model, SikuRoBERTa-BiLSTM-CRF. The proposed approach effectively cap-tures contextual dependencies and improves entity boundary recognition. Experimental re-sults show that our method achieves consistent improvements across almost all evaluation met-rics, highlighting its robustness and effective-ness in handling ancient Chinese texts.
%R 10.18653/v1/2025.alp-1.27
%U https://aclanthology.org/2025.alp-1.27/
%U https://doi.org/10.18653/v1/2025.alp-1.27
%P 206-212

Markdown (Informal)

[Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese](https://aclanthology.org/2025.alp-1.27/) (Zhang et al., ALP 2025)

• Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese (Zhang et al., ALP 2025)

ACL

• Yunmeng Zhang, Meiling Liu, Hanqi Tang, Shige Lu, and Lang Xue. 2025. Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese. In Proceedings of the Second Workshop on Ancient Language Processing, pages 206–212, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.