What has already been done: As part of its Semantic Scholar service, which allows the scientific community to easily search through academic literature, AI2 has already processed the new corpus using the same information extraction and analysis techniques that it applies to all new research. It’s surfacing key pieces of information such as authors, methods, data, and citations to make it easier for scientists to quickly evaluate how each paper adds to the existing research.

It’s also using state-of-the-art natural-language models like ELMo and BERT to map out the similarities between papers. This map is now powering a new feature on Semantic Scholar that allows researchers to create a personalized research feed based on their interests.

Why it matters: Scientists are rushing against the clock to answer pressing questions about the nature of the virus in hopes of stemming its spread. The database not only helps them consolidate existing research in one place but also makes the body of literature easier to mine for insights with natural-language processing algorithms. The OSTP has launched an open call for AI researchers to develop new techniques for text and data mining that will help the medical community comb through the mass of information faster.