Using Text Analytics for Health to Get Meaningful Insights from a Corpus of COVID Scientific Papers
This work addresses the problem of information overload for researchers dealing with the vast COVID-19 literature, though it is incremental as it applies existing methods to new data.
The authors tackled the challenge of navigating the large corpus of COVID-19 scientific papers by using Text Analytics for Health and cloud tools to extract knowledge and build a tool for researchers, resulting in a system that helps derive insights from around 700,000 papers.
Since the beginning of COVID pandemic, there have been around 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus -- and therefore developing AI-based tools to help navigating this corpus and deriving some useful insights from it is highly needed. In this paper, we will use Text Analytics for Health pre-trained service together with some cloud tools to extract some knowledge from scientific papers, gain insights, and build a tool to help researcher navigate the paper collection in a meaningful way.