ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
Bonan Min, Benjamin Rozonoyer, Haoling Qiu, Alexander Zamanian, Nianwen Xue, Jessica MacBride
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 COVID-19 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. A demonstration video is available at https://vimeo.com/528619007.
BibTeX
@inproceedings{min-etal-2021-excavatorcovid,
title = "{E}xcavator{C}ovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for {COVID}-19",
author = "Min, Bonan and
Rozonoyer, Benjamin and
Qiu, Haoling and
Zamanian, Alexander and
Xue, Nianwen and
MacBride, Jessica",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.8/",
doi = "10.18653/v1/2021.emnlp-demo.8",
pages = "63--71"
}