We present a pipeline for a statistical textual exploration, offering a stylometry-based explanation and statistical validation of a hypothesized partition of a text. Given a parameterization of the text, our pipeline: (1) detects literary features yielding the optimal overlap between the hypothesized and unsupervised partitions, (2) performs a hypothesis-testing analysis to quantify the statistical significance of the optimal overlap, while conserving implicit correlations between units of text that are more likely to be grouped, and (3) extracts and quantifies the importance of features most responsible for the classification, estimates their statistical stability and cluster-wise abundance. We apply our pipeline to the first two books in the Bible, where one stylistic component stands out in the eyes of biblical scholars, namely, the Priestly component. We identify and explore statistically significant stylistic differences between the Priestly and non-Priestly components.
|Original language||American English|
|Title of host publication||Findings of the Association for Computational Linguistics, ACL 2023|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||23|
|State||Published - 2023|
|Event||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada|
Duration: 9 Jul 2023 → 14 Jul 2023
|Name||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|Conference||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023|
|Period||9/07/23 → 14/07/23|
Bibliographical notePublisher Copyright:
© 2023 Association for Computational Linguistics.