ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
This system addresses the challenge for government agencies and analysts in processing vast amounts of COVID-19-related information to make timely decisions, though it appears incremental as it applies existing extraction methods to a new domain.
The authors tackled the problem of information overload in COVID-19 event reporting by developing ExcavatorCovid, a machine reading system that extracts events and relations from text to build a Temporal and Causal Analysis Graph, aiming to help policymakers respond more effectively.
Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. An interactive TCAG visualization is available at http://afrl402.bbn.com:5050/index.html. We also released a demonstration video at https://vimeo.com/528619007.