Automatic Detection of Complex Quotation Patterns in Aggadic Literature

TL;DR

This paper introduces ACT, a three-stage algorithm that significantly improves the automatic detection of complex biblical quotations in Rabbinic texts, advancing digital humanities and computational philology.

Contribution

It presents a novel, morphology-aware, context-sensitive algorithm that outperforms existing systems in detecting complex citation patterns in Aggadic literature.

Findings

ACT achieves an F1 score of 0.91, outperforming baselines.

Full ACT pipeline (ACT-QE) has superior recall and precision.

Different configurations of ACT reveal tradeoffs between recall and precision.

Abstract

This paper presents ACT (Allocate Connections between Texts), a novel three-stage algorithm for the automatic detection of biblical quotations in Rabbinic literature. Unlike existing text reuse frameworks that struggle with short, paraphrased, or structurally embedded quotations, ACT combines a morphology-aware alignment algorithm with a context-sensitive enrichment stage that identifies complex citation patterns such as "Wave" and "Echo" quotations. Our approach was evaluated against leading systems, including Dicta, Passim, Text-Matcher, as well as human-annotated critical editions. We further assessed three ACT configurations to isolate the contribution of each component. Results demonstrate that the full ACT pipeline (ACT-QE) outperforms all baselines, achieving an F1 score of 0.91, with superior Recall (0.89) and Precision (0.94). Notably, ACT-2, which lacks stylistic enrichment, achieves higher Recall (0.90) but suffers in Precision, while ACT-3, using longer n-grams, offers a tradeoff between coverage and specificity. In addition to improving quotation detection, ACT's ability to classify stylistic patterns across corpora opens new avenues for genre classification and intertextual analysis. This work contributes to digital humanities and computational philology by addressing the methodological gap between exhaustive machine-based detection and human editorial judgment. ACT lays a foundation for broader applications in historical textual analysis, especially in morphologically rich and citation-dense traditions like Aggadic literature.