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        <title>Multi-Strategy Named Entity Recognition System for Ancient Chinese</title>
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            <title>Proceedings of the Second Workshop on Ancient Language Processing</title>
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            <namePart type="given">Adam</namePart>
            <namePart type="family">Anderson</namePart>
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            <namePart type="given">Bin</namePart>
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    <abstract>We present a multi-strategy Named Entity Recognition (NER) system for ancient Chi-nese texts in EvaHan2025. Addressing dataset heterogeneity, we use a Conditional Random Field (CRF) for Tasks A and C to handle six entity types’ complex dependencies, and a lightweight Softmax classifier for Task B’s simpler three-entity tagset. Ablation studies on training data confirm CRF’s superiority in capturing sequence dependencies and Softmax’s computational advantage for simpler tasks. On blind tests, our system achieves F1-scores of 83.94%, 88.31%, and 82.15% for Test A, B, and C—outperforming baselines by 2.46%, 0.81%, and 9.75%. With an overall F1 improvement of 4.30%, it excels across historical and medical domains. This adaptability enhances knowledge extraction from ancient texts, offering a scalable NER framework for low-resource, complex languages.</abstract>
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    <identifier type="doi">10.18653/v1/2025.alp-1.28</identifier>
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%0 Conference Proceedings
%T Multi-Strategy Named Entity Recognition System for Ancient Chinese
%A Dong, Wenxuan
%A Liu, Meiling
%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 dong-liu-2025-multi
%X We present a multi-strategy Named Entity Recognition (NER) system for ancient Chi-nese texts in EvaHan2025. Addressing dataset heterogeneity, we use a Conditional Random Field (CRF) for Tasks A and C to handle six entity types’ complex dependencies, and a lightweight Softmax classifier for Task B’s simpler three-entity tagset. Ablation studies on training data confirm CRF’s superiority in capturing sequence dependencies and Softmax’s computational advantage for simpler tasks. On blind tests, our system achieves F1-scores of 83.94%, 88.31%, and 82.15% for Test A, B, and C—outperforming baselines by 2.46%, 0.81%, and 9.75%. With an overall F1 improvement of 4.30%, it excels across historical and medical domains. This adaptability enhances knowledge extraction from ancient texts, offering a scalable NER framework for low-resource, complex languages.
%R 10.18653/v1/2025.alp-1.28
%U https://aclanthology.org/2025.alp-1.28/
%U https://doi.org/10.18653/v1/2025.alp-1.28
%P 213-220

Markdown (Informal)

[Multi-Strategy Named Entity Recognition System for Ancient Chinese](https://aclanthology.org/2025.alp-1.28/) (Dong & Liu, ALP 2025)

• Multi-Strategy Named Entity Recognition System for Ancient Chinese (Dong & Liu, ALP 2025)

ACL

• Wenxuan Dong and Meiling Liu. 2025. Multi-Strategy Named Entity Recognition System for Ancient Chinese. In Proceedings of the Second Workshop on Ancient Language Processing, pages 213–220, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.