Researchers have developed a new workflow for identifying essay-scale republication and reuse of fragmented historical texts, focusing on the works of eighteenth-century philosopher David Hume. The study compares a staged rule-based workflow against direct LLM settings and automated rule adaptation. The proposed workflow achieved a high F1 score on labeled data and demonstrated a strong precision-recall trade-off, effectively consolidating evidence into plausible transmission relations. This method provides a practical approach for creating compact candidate spaces for historical analysis, even with incomplete ground truth. AI
IMPACT This research demonstrates a novel application of LLMs for historical text analysis, potentially improving the accuracy and efficiency of digital humanities research.
RANK_REASON This is a research paper detailing a new methodology for text reuse analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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