Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence is available from https://github.com/drgriffis/text-essence.
- Pub Date:
- March 2021
- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Human-Computer Interaction
- Accepted as a Systems Demonstration at NAACL-HLT 2021. Video demonstration at https://youtu.be/1xEEfsMwL0k