@inproceedings{tian-guo-2022-ancient,
    title = "{A}ncient {C}hinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation",
    author = "Tian, Yanzhi  and
      Guo, Yuhang",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.lt4hala-1.21/",
    pages = "146--149",
    abstract = "We attended the EvaHan2022 ancient Chinese word segmentation and Part-of-Speech (POS) tagging evaluation. We regard the Chinese word segmentation and POS tagging as sequence tagging tasks. Our system is based on a BERT-BiLSTM-CRF model which is trained on the data provided by the EvaHan2022 evaluation. Besides, we also employ data augmentation techniques to enhance the performance of our model. On the Test A and Test B of the evaluation, the F1 scores of our system achieve 94.73{\%} and 90.93{\%} for the word segmentation, 89.19{\%} and 83.48{\%} for the POS tagging."
}