ViS-Á-ViS: Detecting Similar Patterns in Annotated Literary Text

Moshe Schorr, Oren Mishali, Benny Kimelfeld, Ophir Münz-Manor

Research output: Contribution to journalArticlepeer-review


We present a web-based system called ViS-Á-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.
Original languageEnglish
Number of pages5
JournalarXiv preprint
StateE-pub ahead of print - 2020


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