Semantic Scholar

Semantic Scholar
Semantic Scholar logo.svg
Type of site
Search engine
Created byAllen Institute for Artificial Intelligence
LaunchedNovember 2015 (2015-11)

Semantic Scholar is an artificial intelligence–powered research tool for scientific literature developed at the Allen Institute for AI and publicly released in November 2015.[1] It uses advances in natural language processing to provide summaries for scholarly papers.[2] The Semantic Scholar team is actively researching the use of artificial-intelligence in natural language processing, machine learning, Human-Computer interaction, and information retrieval.[3]

Semantic Scholar began as a database surrounding the topics of computer science, geoscience, and neuroscience.[4] However, in 2017 the system began including biomedical literature in its corpus.[4] As of September 2022, they now include over 200 million publications from all fields of science.[5]

  1. ^ Eunjung Cha, Ariana (3 November 2015). "Paul Allen's AI research group unveils program that aims to shake up how we search scientific knowledge. Give it a try". The Washington Post. Archived from the original on 6 November 2019. Retrieved November 3, 2015.
  2. ^ Hao, Karen (November 18, 2020). "An AI helps you summarize the latest in AI". MIT Technology Review. Retrieved 2021-02-16.
  3. ^ "Semantic Scholar Research". Retrieved 2021-11-22.
  4. ^ a b Fricke, Suzanne (2018-01-12). "Semantic Scholar". Journal of the Medical Library Association. 106 (1): 145–147. doi:10.5195/jmla.2018.280. ISSN 1558-9439. S2CID 45802944.
  5. ^ Matthews, David (1 September 2021). "Drowning in the literature? These smart software tools can help". Nature. Retrieved 5 September 2022. ...the publicly available corpus compiled by Semantic Scholar — a tool set up in 2015 by the Allen Institute for Artificial Intelligence in Seattle, Washington — amounting to around 200 million articles, including preprints.

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