Ancient Chinese Glyph Identification Powered by Radical Semantics

Yang Chi, Fausto Giunchiglia, Chuntao Li, Hao Xu


Abstract
The ancestor of Chinese character – the ancient characters from about 1300 BC to 200 BC are not fixed in their writing glyphs. At the same or different points in time, one character can possess multiple glyphs that are different in shapes or radicals. Nearly half of ancient glyphs have not been deciphered yet. This paper proposes an innovative task of ancient Chinese glyph identification, which aims at inferring the Chinese character label for the unknown ancient Chinese glyphs which are not in the training set based on the image and radical information. Specifically, we construct a Chinese glyph knowledge graph (CGKG) associating glyphs in different historical periods according to the radical semantics, and propose a multimodal Chinese glyph identification framework (MCGI) fusing the visual, textual, and the graph data. The experiment is designed on a real Chinese glyph dataset spanning over 1000 years, it demonstrates the effectiveness of our method, and reports the potentials of each modality on this task. It provides a preliminary reference for the automatic ancient Chinese character deciphering at the glyph level.
Anthology ID:
2024.findings-acl.718
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12065–12074
Language:
URL:
https://aclanthology.org/2024.findings-acl.718
DOI:
10.18653/v1/2024.findings-acl.718
Bibkey:
Cite (ACL):
Yang Chi, Fausto Giunchiglia, Chuntao Li, and Hao Xu. 2024. Ancient Chinese Glyph Identification Powered by Radical Semantics. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12065–12074, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Ancient Chinese Glyph Identification Powered by Radical Semantics (Chi et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-acl.718.pdf