Abstract
Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare. Ancient text division and lexical annotation are important parts of classical literature comprehension, and previous studies have tried to construct auxiliary dictionary and other fused knowledge to improve the performance. In this paper, we propose a framework for ancient Chinese Word Segmentation and Part-of-Speech Tagging that makes a twofold effort: on the one hand, we try to capture the wordhood semantics; on the other hand, we re-predict the uncertain samples of baseline model by introducing external knowledge. The performance of our architecture outperforms pre-trained BERT with CRF and existing tools such as Jiayan.- Anthology ID:
- 2022.lt4hala-1.25
- 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:
- 164–168
- Language:
- URL:
- https://aclanthology.org/2022.lt4hala-1.25
- DOI:
- Cite (ACL):
- Pengyu Wang and Zhichen Ren. 2022. The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS. In Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages, pages 164–168, Marseille, France. European Language Resources Association.
- Cite (Informal):
- The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS (Wang & Ren, LT4HALA 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.lt4hala-1.25.pdf