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    <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.</abstract>
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%0 Conference Proceedings
%T Vector Poetics: Parallel Couplet Detection in Classical Chinese Poetry
%A Kurzynski, Maciej
%A Xu, Xiaotong
%A Feng, Yu
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Miyagawa, So
%Y Alnajjar, Khalid
%Y Bizzoni, Yuri
%S Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, USA
%F kurzynski-etal-2024-vector
%X 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.
%U https://aclanthology.org/2024.nlp4dh-1.19
%P 200-208

Markdown (Informal)

[Vector Poetics: Parallel Couplet Detection in Classical Chinese Poetry](https://aclanthology.org/2024.nlp4dh-1.19) (Kurzynski et al., NLP4DH 2024)

• Vector Poetics: Parallel Couplet Detection in Classical Chinese Poetry (Kurzynski et al., NLP4DH 2024)

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.