Cite and Seek: Automated Literary Reference Mining at Corpus Scale

Identifying intertextual references in literature is a complex task that requires both linguistic and contextual understanding. This study presents a methodology for extracting explicit intertextual references between books, focusing on explicit mentions of book titles, character names, and signature quotes. In contrast to scientific citation analysis, which relies on structured references, literary references are often implicit and require deeper semantic interpretation. We propose a multiphase approach that combines manual annotation, AI-assisted validation, and systematic disagreement analysis to construct a high-quality dataset of positive and negative examples from a corpus of approximately 30,000 public-domain books. We evaluate the performance of individual annotators and introduce an aggregate annotator that achieves an F1 score above 0.96 across all reference types. Our methodology supports a rigorous annotation process that reduces both false positives and false negatives. The resulting dataset offers a foundation for training and evaluating models capable of detecting literary connections, enabling deeper insights into intertextual influences in narrative literature.