COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System

Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji


Abstract
To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis. We provide a comprehensive structured view of claims, including rich claim attributes (such as claimers and claimer affiliations) and associated knowledge elements as claim semantics (such as events, relations and entities), enabling users to explore equivalent, refuting, or supporting claims with structural evidence, such as shared claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata as the hub to merge coreferential knowledge elements. The system automatically provides users a comprehensive exposure to COVID-19 related claims, their importance, and their interconnections. The system is publicly available at GitHub and DockerHub, with complete documentation.
Anthology ID:
2022.acl-demo.13
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
135–144
Language:
URL:
https://aclanthology.org/2022.acl-demo.13
DOI:
10.18653/v1/2022.acl-demo.13
Bibkey:
Cite (ACL):
Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, and Heng Ji. 2022. COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 135–144, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (Li et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-demo.13.pdf
Code
 uiucnlp/covid-claim-radar