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

Siwen Wang (), Yuxuan Liu, Bo Chen and Xiaobing Zhao
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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

A chapter in Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), 2026, pp 83-102 from Springer

Abstract: 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.

Keywords: animal metaphor; plant metaphor; automatic metaphor mining (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-689-0_9

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