Automatic Mining and Comparative Analysis of Animal and Plant Metaphors in Chinese and Western Classical Texts
- DOI
- 10.2991/978-94-6239-689-0_9How to use a DOI?
- Keywords
- animal metaphor; plant metaphor; automatic metaphor mining
- 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.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Siwen Wang AU - Yuxuan Liu AU - Bo Chen AU - Xiaobing Zhao PY - 2026 DA - 2026/05/28 TI - Automatic Mining and Comparative Analysis of Animal and Plant Metaphors in Chinese and Western Classical Texts BT - Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026) PB - Atlantis Press SP - 83 EP - 102 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6239-689-0_9 DO - 10.2991/978-94-6239-689-0_9 ID - Wang2026 ER -