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Turnitin (stylized as turnitin) is an Internet-based similarity detection service run by the American company Turnitin, LLC, a subsidiary of Advance Publications. Founded in 1998, it sells its licenses to universities and high schools who then use the software as a service (SaaS) website to check submitted documents against its database and the ...
Grammarly is an English language writing assistant software tool. It reviews the spelling, grammar, and tone of a piece of writing as well as identifying possible instances of plagiarism . It can also suggest style and tonal recommendations to users and produce writing from prompts with its generative AI capabilities.
Turnitin's parent company iParadigms employs almost 100 people. It is backed by the private equity firm Warburg Pincus. It has 8 global offices serving almost 130 countries. It is headquartered in Oakland, California. Turnitin is one of the largest and most well known companies that provides this type of service.
As of August 2021, the group owns Condé Nast—which includes the magazines Vogue, The New Yorker, and Wired—Turnitin, The Ironman Group, Advance Local, American City Business Journals, Stage Entertainment, Leaders Group, and the Seattle-based digital agency Pop, Inc., and is a large shareholder in Reddit. [9] [10]
Turnitin checks and archives millions of papers and uses its database and algorithms to identify plagiarized material. [1]Submissions are compared to over 17 billion web pages, 200 million student papers, and over 100 million additional articles from content publishers, including library databases, text-books, digital reference collections, subscription-based publications, homework helper ...
At off-peak hours for Turnitin, they could run full reports of every single article on English Wikipedia. The reports would detail which parts of Wikipedia articles matched web content, proprietary content, and, if desired, prior submissions to Turnitin. The reports would identify which external source positively overlapped for each match.
More recent approaches to assess content similarity using neural networks have achieved significantly greater accuracy, but come at great computational cost. [36] Traditional neural network approaches embed both pieces of content into semantic vector embeddings to calculate their similarity, which is often their cosine similarity.
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