COVIDScholar: An NLP hub for COVID-19 research literature

The urgency of dealing with COVID-19 requires knowledge gathering and dissemination in novel ways. Building on our previous work using natural language processing (NLP) to extract latent knowledge from literature in the physical sciences, we have set out to apply similar techniques to the COVID-19 literature. As part of this effort we will build a knowledge portal tailored specifically for the needs of COVID-19 researchers that leverages state of the art NLP techniques to synthesize the information spread across tens of thousands of emergent research articles, patents, and clinical trials into actionable insights and new knowledge. We plan to create the largest and most current database of research findings for COVID-19 related work, automatically updated to remain current, and make this text data easily accessible to the research community. Moreover, we will use NLP to design unique search tools powered by custom machine learning models that allow them to engage with the literature more effectively and complete their research faster. Using our team's expertise in large-scale data acquisition from diverse sources, natural language processing, and genomics, and engaging deeply with the biology and genomics community for feedback and guidance, we have already made significant progress towards this goal, letting users search within more COVID-related literature than any other repository through our prototype website (covidscholar.com).