Exploration and Discovery of the COVID-19 Literature through Semantic Visualization

Jingxuan Tu, Marc Verhagen, Brent Cochran, James Pustejovsky

Abstract
We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.
Anthology ID:
2021.naacl-srw.11
Original:
2021.naacl-srw.11v1
Version 2:
2021.naacl-srw.11v2
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2021
Address:
Online
Editors:
Esin Durmus, Vivek Gupta, Nelson Liu, Nanyun Peng, Yu Su
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–87
Language:
URL:
https://aclanthology.org/2021.naacl-srw.11
DOI:
10.18653/v1/2021.naacl-srw.11
Bibkey:
Cite (ACL):
Jingxuan Tu, Marc Verhagen, Brent Cochran, and James Pustejovsky. 2021. Exploration and Discovery of the COVID-19 Literature through Semantic Visualization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 76–87, Online. Association for Computational Linguistics.
Cite (Informal):
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization (Tu et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2021.naacl-srw.11.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-1/2021.naacl-srw.11.mp4
Data
CORD-19
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