Automatic Detection of Complex Quotation Patterns in Aggadic Literature
Hadar Miller, Tsvi Kuflik, Moshe Lavee

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,…
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Taxonomy
TopicsText Readability and Simplification · Topic Modeling · Digital Humanities and Scholarship
