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Automatic Mining and Comparative Analysis of Animal and Plant Metaphors in Chinese and Western Classical Texts

In: Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)

Author

Listed:
  • Siwen Wang

    (Minzu University of China, School of Chinese Ethnic Minority Languages and Literatures)

  • Yuxuan Liu

    (Minzu University of China, School of Chinese Ethnic Minority Languages and Literatures)

  • Bo Chen

    (Minzu University of China, School of Information Engineering)

  • Xiaobing Zhao

    (Minzu University of China, School of Information Engineering)

Abstract

This paper proposes a large language model (LLM)–based automatic mining framework for identifying and analyzing animal and plant metaphors in Chinese and Western classical texts. Treating metaphor identification as a structured information extraction task, we design a multi-stage pipeline integrating prompt-guided entity extraction, contextual metaphor classification, semantic labeling, and human-in-the-loop verification. Using Chu Ci and Aesop’s Fables as case studies, we construct a validated metaphor dataset containing 2,172 instances. Experimental analysis shows that the proposed framework achieves high coverage while substantially reducing manual annotation effort. The results demonstrate the feasibility of applying LLM-driven computational methods to cross-cultural metaphor research in digital humanities.

Suggested Citation

  • Siwen Wang & Yuxuan Liu & Bo Chen & Xiaobing Zhao, 2026. "Automatic Mining and Comparative Analysis of Animal and Plant Metaphors in Chinese and Western Classical Texts," Advances in Economics, Business and Management Research, in: Ljiljana Trajkovic & José Alfredo F. Costa & Zaher Al Aghbari & Nor Azman Ismail & Dariusz Jacek Jak (ed.), Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), pages 83-102, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-689-0_9
    DOI: 10.2991/978-94-6239-689-0_9
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