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.- Anthology ID:
- 2022.lt4hala-1.21
- Volume:
- Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LT4HALA
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 146–149
- Language:
- URL:
- https://aclanthology.org/2022.lt4hala-1.21
- DOI:
- Cite (ACL):
- Yanzhi Tian and Yuhang Guo. 2022. Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation. In Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages, pages 146–149, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation (Tian & Guo, LT4HALA 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.lt4hala-1.21.pdf