Detecting “Parasitic Poems”: Quantifying Poetic Style in Late Imperial Chinese Fiction
Presentation by Dr Keli Du and colleagues as part of CHR2025
Date:
12.12.2025 bis 12.12.2025Place:
09.–12.12.2025
Luxembourg Centre for Contemporary and Digital History (C²DH) at the University of Luxembourg
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Dr. Keli DuFurther Information:
CHR2025The Computational Humanities Research (CHR) community is an international and interdisciplinary community that supports researchers with an interest in computational approaches to the humanities. The 2025 edition of the Computational Humanities Research conference will take place on December 9-12, 2025 at the Luxembourg Centre for Contemporary and Digital History (C²DH) at the University of Luxembourg.
Detecting “Parasitic Poems”: Quantifying Poetic Style in Late Imperial Chinese Fiction
Jiayu Liu (University of Illinois Urbana-Champaign, Rongqian Ma (Indiana University Bloomington) and Keli Du (Trier University)
Embedded poetry is a defining feature of late imperial Chinese fiction, yet its narrative function remains contested. While some critics regard these poems as “parasitic”—reiterating surrounding prose with minimal contribution—others argue for their integral aesthetic and rhetorical roles. This study aims to explore if parasitic poems exist in late imperial Chinese fiction and how they can be systematically identified. We develop a computational framework to detect such poems across a corpus of Qing-dynasty novels, combining proxy-based measures (cosine similarity and mutual information) with prompt-based large language models (LLMs). Using a manually annotated dataset of 300 poem-context pairs, we evaluate each method’s alignment with human judgments. Our preliminary findings show that proxy models achieve higher accuracy but exhibit limited sensitivity to nonparasitic cases. A multilingual prompt-based approach yields a more balanced performance, suggesting LLMs can approximate literary interpretation when effectively prompted. Our work offers tools for analyzing Chinese poetry and demonstrates the potential of LLMs in modeling literary analysis.