COVIDScholar: “New AI Tool Searches Thousands of Scientific Papers to Guide Researchers to Coronavirus Insights”

From a Post on The Conversation by:

Traditional methods of searching through the research literature just don’t cut it anymore. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists. AI can’t replace scientists, but it can help them gain new insights from more papers than they could read in a lifetime.

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The most important part of our work is the data. We’ve built web scrapers that collect new papers as they’re published from a wide variety of sources, making them available on our website within 15 minutes of their appearance online. We also clean the data, fixing mistakes in formatting and comparing the same paper from multiple sources to find the best version. Our machine learning algorithms then go to work on the paper, tagging it with subject categories and marking work important to COVID-19.

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See Also: “Google’s New AI-Powered Search Tool Helps Researchers With Coronavirus Queries” (May 7, 2020)

See Also: Search COVID-19 Research via Semantic Scholar

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