Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation

Xiaomeng Pan, Hongfei Wang, Teruaki Oka, Mamoru Komachi


Abstract
Word Sense Disambiguation (WSD) is a core task in Natural Language Processing (NLP). Ancient Chinese has rarely been used in WSD tasks, however, as no public dataset for ancient Chinese WSD tasks exists. Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriate sense in a context is difficult and time-consuming owing to the different usages in ancient and modern Chinese. Actually, no public dataset for ancient Chinese WSD tasks exists. To solve the problem of ancient Chinese WSD, we annotate part of Pre-Qin (221 BC) text Zuo Zhuan using a copyright-free dictionary to create a public sense-tagged dataset. Then, we apply a simple Nearest Neighbors (k-NN) method using a pre-trained language model to the dataset. Our code and dataset will be available on GitHub.
Anthology ID:
2022.naacl-srw.17
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Month:
July
Year:
2022
Address:
Hybrid: Seattle, Washington + Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
129–135
Language:
URL:
https://aclanthology.org/2022.naacl-srw.17
DOI:
10.18653/v1/2022.naacl-srw.17
Bibkey:
Cite (ACL):
Xiaomeng Pan, Hongfei Wang, Teruaki Oka, and Mamoru Komachi. 2022. Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 129–135, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
Cite (Informal):
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation (Pan et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-srw.17.pdf
Video:
 https://aclanthology.org/2022.naacl-srw.17.mp4