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Germany's @Blinkist Raises $18.8M for Micro-learning App and Platform

Germany's @Blinkist Raises $18.8M for Micro-learning App and Platform | Language Tech Market News | Scoop.it

Berlin-based startup Blinkist, which transforms non-fiction books into 15-minute audio and text-based digests, has raised $18.8 million in a funding round led by Insight Venture Partners. The company will use the money to expand its team and introduce support for new languages for its app in addition to English and German. The audio and text-based insights aim to outline the main ideas of more than 2,500 books in different genres, including entrepreneurship, politics, science, psychology, investment, and parenting.

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@elsevier: New STM Content Resource to Boost Text Mining and #NLP Tool Production

@elsevier: New STM Content Resource to Boost Text Mining and #NLP Tool Production | Language Tech Market News | Scoop.it

To ihelp facilitate the development of NLP tools in an open environment, Elsevier has provided a selection of 110 journal articles from 10 different STM domains as a freely-available and redistributable corpus. The articles were selected from open access articles published by Elsevier and licensed with a Creative Commons CC-BY license. The domains are agriculture, astronomy, biology, chemistry, computer science, earth science, engineering, materials science math and medicine. Currently we provide 11 full-text articles in each of the 10 domains.

LT-Innovate's insight:

Many open-source NLP tools are not suited to in-depth science knowledge mining.

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@Taylor&Francis Partner with @UNSILO to Deliver Content Discoverability

Publishers Taylor & Francis is partnering with AI technology providers UNSILO (DK) to help enrich customers’ online experiences. Users of taylorfrancis.com are being shown recommendations of additional, highly relevant content based semantically on what they are already reading. This will enable users to discover new research more easily.

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The Augmented Researcher: What Does 2018 Hold for AI in Science Publishing?

The Augmented Researcher: What Does 2018 Hold for AI in Science Publishing? | Language Tech Market News | Scoop.it
Semantic data is important to R&D, because it means we can link facts that are related across papers, and over different domains of knowledge – enabling us to deliver insights that might not be obvious from reading one paper alone. To do so requires normalizing the terminology with taxonomies, to allow a network to be created. The increasing reliance on linked facts mean the demands of the modern researcher are changing; researchers need bespoke analytics products for their specific needs, built on robust semantic databases. In today’s world, this more often than not means solutions that combine semantic technology methods, augmented with machine learning and machine reasoning approaches.
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