Computational Discovery of Chiasmus in Ancient Religious Text
This addresses a long-standing scholarly debate in religious studies by providing a systematic tool for analyzing ancient texts, though it is incremental as it applies existing computational methods to a new domain.
The paper tackles the problem of detecting chiasmus, a debated literary device, in Biblical texts by introducing the first computational approach using neural embeddings, achieving system precision@k of 0.80 at the verse level and 0.60 at the half-verse level.
Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system precision@k of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.