More Questions Than Answers: Using NLP to Fight COVID-19

Explore how Natural Language Processing (NLP) techniques are utilized to assist medical domain experts in navigating information from over 57,000 scholarly articles related to coronaviruses. Learn about the development of an annotation tool that enables domain experts to contribute their knowledge within a system architecture allowing parallel validation of new models. Discover the combination of clustering, topic modeling, reference extraction, and Elasticsearch techniques to enable efficient information retrieval. This 30-minute conference talk, presented by Luna Feng and Conner Cowling at the Toronto Machine Learning Series (TMLS), offers insights into leveraging NLP to combat COVID-19 and enhance the accessibility of crucial medical research.

Luna Feng & Conner Cowling - More Questions Than Answers: Using NLP to fight COVID-19