Difference between revisions of "Natural Language Generation (NLG)"

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** [http://www.phrasetech.com/ PhraseTech]
 
** [http://www.phrasetech.com/ PhraseTech]
 
** [http://www.retresco.de/en/ Retresco]
 
** [http://www.retresco.de/en/ Retresco]
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** [http://www.salesforce.com/products/einstein-analytics/overview/ Salesforce] Einstein Analytics platform
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** [http://www.sapanalytics.cloud/product/ SAP] Analytics Cloud
 
** [http://specif.io/features/ Specifio]
 
** [http://specif.io/features/ Specifio]
 
** [http://www.syllabs.com/en/ Syllabs]
 
** [http://www.syllabs.com/en/ Syllabs]

Revision as of 15:55, 31 December 2019

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Natural-language generation (NLG) is the natural-language processing task of generating natural language from a machine-representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations. It could be said an NLG system is like a translator that converts data into a natural-language representation. However, the methods to produce the final language are different from those of a compiler due to the inherent expressivity of natural languages. ...NLG may be viewed as the opposite of natural-language understanding: whereas in natural-language understanding, the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a concept into words. Wikipedia


Mapping the Field of Algorithmic Journalism | Konstantin N. Dörr

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Augmented Analytics

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An augmented analytics engine can automatically go through a company’s data, clean it, analyze it, and convert these insights into action steps for the executives or marketers with little to no supervision from a technical person. Augmented analytics therefore can make analytics accessible to all SMB owners. Augmented Analytics Demystified | Bill Su - Medium