Abstract

The unprecedented global pandemic related to the spread of the coronavirus SARS-COV-2 and the associated outbreak of the infection dubbed COVID-19 has had dramatic impacts worldwide during 2020. Scientists around the globe have responded to the pandemic, hoping to make some contribution to understanding, tracking, modeling, and/or responding. The ACL community can play a unique role in supporting research to combat COVID-19. Valuable insights and critical information may be contained in vast quantities of unstructured text and speech data. Thousands of previously published research articles (and those being published on a daily basis) on coronavirus may shape our understanding of the virus or support best practice clinical management of the disease. Analysis of millions of social media posts may help us understand how the public at large is responding to the outbreak. Identifying spreading misinformation can be critical to public health messaging. Automatic identification and organization of helpful information collected from the web might aid public response.
The impetus behind organizing this “emergency” workshop was to highlight the myriad ways in which Natural Language Processing (NLP) could be used to respond to the COVID-19 pandemic, and the ACL community rose to the challenge, supported by resources such as the CORD-19 dataset from the Allen Institute for AI which was used for a Kaggle challenge 1. We are pleased to have one of the first papers introducing this important data set amongst our accepted papers [1]. We announced the workshop on April 03, 2020 and immediately created an OpenReview …

Date

2020

Authors

Karin Verspoor, K Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron C Wallace

Conference

Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020