Abstract
This paper explores computational approaches for detecting parallelism in classical Chinese poetry, a rhetorical device where two verses mirror each other in syntax, meaning, tone, and rhythm. We experiment with five classification methods: (1) verb position matching, (2) integrated semantic, syntactic, and word-segmentation analysis, (3) difference-based character embeddings, (4) structured examples (inner/outer couplets), and (5) GPT-guided classification. We use a manually annotated dataset, containing 6,125 pentasyllabic couplets, to evaluate performance. The results indicate that parallelism detection poses a significant challenge even for powerful LLMs such as GPT-4o, with the highest F1 score below 0.72. Nevertheless, each method contributes valuable insights into the art of parallelism in Chinese poetry, suggesting a new understanding of parallelism as a verbal expression of principal components in a culturally defined vector space.- Anthology ID:
- 2024.nlp4dh-1.19
- Volume:
- Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
- Month:
- November
- Year:
- 2024
- Address:
- Miami, USA
- Editors:
- Mika Hämäläinen, Emily Öhman, So Miyagawa, Khalid Alnajjar, Yuri Bizzoni
- Venue:
- NLP4DH
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 200–208
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.nlp4dh-1.19/
- DOI:
- 10.18653/v1/2024.nlp4dh-1.19
- Cite (ACL):
- Maciej Kurzynski, Xiaotong Xu, and Yu Feng. 2024. Vector Poetics: Parallel Couplet Detection in Classical Chinese Poetry. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 200–208, Miami, USA. Association for Computational Linguistics.
- Cite (Informal):
- Vector Poetics: Parallel Couplet Detection in Classical Chinese Poetry (Kurzynski et al., NLP4DH 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.nlp4dh-1.19.pdf