ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19

TL;DR

ExcavatorCovid is a machine reading system that extracts COVID-19 related events and their causal and temporal relations from open-source texts to aid policymakers in understanding pandemic dynamics and decision impacts.

Contribution

The paper introduces ExcavatorCovid, a novel system that automatically constructs a Temporal and Causal Analysis Graph from diverse text sources for COVID-19.

Findings

Successfully extracts COVID-19 events and relations from texts.

Builds an interactive visualization of the event graph.

Aids policymakers in timely pandemic response.

Abstract

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.