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AI2’s Semantic Scholar expands to cover 175 million papers in all scientific disciplines

There are a lot of scientific papers out there, and finding the right ones, or the right connections between them, can be extremely difficult. Semantic Scholar uses AI to understand and index journal articles, but until recently has been limited to a handful of topics. It has now expanded to cover practically every branch of science — and some 175 million papers.
I covered Semantic Scholar, a project of the Allen Institute for AI, when it first launched in 2016, at which time it had only indexed papers in computer science and neuroscience. The next year, it added biomedical papers covering a variety of sub-topics.
The problem they are attempting to solve is simply that there’s too much information for academics to parse. And while they may do their best to keep up with the literature, a key insight or relevant result may be hidden away in an obscure journal that only gets the vaguest reference in a citation or review.
“We created it because of information overload in science,” explained project head Doug Raymond in an interview. “The focus of the team was, how do we make science more discoverable?”

Allen Institute for AI’s Semantic Scholar adds biomedical papers to its AI-sorted corpus
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