# ViS-'A-ViS : Detecting Similar Patterns in Annotated Literary Text

arXiv: https://arxiv.org/abs/2009.02063 · HTML: https://mlsanity.com/paper/2009.02063/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)

## Abstract (arXiv)

We present a web-based system called ViS-'A-ViS aiming to assist literary scholars in detecting repetitive patterns in an annotated textual corpus. Pattern detection is made possible using distant reading visualizations that highlight potentially interesting patterns. In addition, the system uses time-series alignment algorithms, and in particular, dynamic time warping (DTW), to detect patterns automatically. We present a case-study where an ancient Hebrew poetry corpus was manually annotated with figurative language devices as metaphors and similes and then loaded into the system. Preliminary results confirm the effectiveness of the system in analyzing the annotated data and in detecting literary patterns and similarities.