ViS-Á-ViS : Detecting Similar Patterns in Annotated Literary Text
This assists literary scholars in analyzing annotated textual corpora, but it is incremental as it applies existing time-series alignment methods to a new domain.
The researchers tackled the problem of detecting repetitive patterns in annotated literary texts by developing a web-based system called ViS-Á-ViS, which uses distant reading visualizations and dynamic time warping algorithms, and preliminary results confirmed its effectiveness in analyzing an ancient Hebrew poetry corpus annotated with figurative language devices.
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.