Combining Lexicon Definitions and the Retrieval-Augmented Generation of a Large Language Model for the Automatic Annotation of Ancient Chinese Poetry

Jiabin Li, Tingxin Wei, Weiguang Qu (), Bin Li, Minxuan Feng and Dongbo Wang
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Jiabin Li: School of Liberal Arts, Nanjing Normal University, Nanjing 210023, China
Tingxin Wei: School of International Culture and Education, Nanjing Normal University, Nanjing 210023, China
Weiguang Qu: School of Liberal Arts, Nanjing Normal University, Nanjing 210023, China
Bin Li: School of Liberal Arts, Nanjing Normal University, Nanjing 210023, China
Minxuan Feng: School of Liberal Arts, Nanjing Normal University, Nanjing 210023, China
Dongbo Wang: School of Information Management, Nanjing Agricultural University, Nanjing 210095, China

Mathematics, 2025, vol. 13, issue 12, 1-19

Abstract: Existing approaches to the automatic annotation of classical Chinese poetry often fail to generate precise source citations and depend heavily on manual segmentation, limiting their scalability and accuracy. To address these shortcomings, we propose a novel paradigm that integrates dictionary retrieval with retrieval-augmented large language model enhancements for automatic poetic annotation. Our method leverages the contextual understanding capabilities of large models to dynamically select appropriate lexical senses and employs an automated segmentation technique to minimize reliance on manual splitting. For poetic segments absent from standard dictionaries, the system retrieves pertinent information from a domain-specific knowledge base and generates definitions grounded in this auxiliary data, thereby substantially improving both annotation accuracy and coverage. The experimental results demonstrate that our approach outperforms general-purpose large language models and pre-trained classical Chinese language models on automatic annotation tasks; notably, it achieves a micro-averaged accuracy of 94.33% on key semantic segments. By delivering more precise and comprehensive annotations, this framework advances the computational analysis of classical Chinese poetry and offers significant potential for intelligent teaching applications and digital humanities research.

Keywords: automatic annotation; knowledge base construction; large language model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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