@inproceedings{dong-liu-2025-multi,
    title = "Multi-Strategy Named Entity Recognition System for {A}ncient {C}hinese",
    author = "Dong, Wenxuan  and
      Liu, Meiling",
    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/fix-sig-urls/2025.alp-1.28/",
    pages = "213--220",
    ISBN = "979-8-89176-235-0",
    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."
}