Computational Discovery of Chiasmus in Ancient Religious Text

TL;DR

This paper presents the first computational method for detecting chiasmus in Biblical texts using neural embeddings, achieving high accuracy and providing insights into this literary device.

Contribution

It introduces a novel neural embedding-based approach for systematic detection of chiasmus in ancient texts, validated with expert review and high precision.

Findings

High inter-annotator agreement

Precision@k of 0.80 at verse level

Effective qualitative analysis of chiasmus distribution

Abstract

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.