Semantic Scholar
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Semantic Scholar is a project developed at the Allen Institute for Artificial Intelligence. Publicly released in November 2015, it is designed to be an AI-backed search engine for academic publications.[1] The project uses a combination of machine learning, natural language processing, and machine vision to add a layer of semantic analysis to the traditional methods of citation analysis, and to extract relevant figures, entities, and venues from papers.[2] In comparison to Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential papers, and to identify the connections between them.
As of January 2018, following a 2017 project that added biomedical papers and topic summaries, the Semantic Scholar corpus included more than 40 million papers from computer science and biomedicine.[3] In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project.[4]
As of August 2019, the number of included papers had grown to more than 173 million[5] after the addition of the Microsoft Academic Graph records.[6] Each paper hosted by Semantic Scholar is assigned a unique identifier called the Semantic Scholar Corpus ID (or S2CID for short), for example
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See also
References
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External links
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