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

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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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[http://www.youtube.com/results?search_query=generate+text+generation+write+sentences+nlg+natural+language+semantics Youtube search...]
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[https://www.youtube.com/results?search_query=ai+nlp+natural+language+Processing+text+nlg+~generate YouTube]
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[http://www.google.com/search?q=generate+text+generation+write+sentences+machine+learning+nlg+natural+language+semantics+machine+learning+ML ...Google search]
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[https://www.quora.com/search?q=ai%20nlp%20natural%20language%20Processing%20text%20nlg%20~generate ... Quora]
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[https://www.google.com/search?q=ai+nlp+natural+language+Processing+text+nlg+~generate ...Google search]
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[https://news.google.com/search?q=ai+nlp+natural+language+Processing+text+nlg+~generate ...Google News]
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[https://www.bing.com/news/search?q=ai+nlp+natural+language+Processing+text+nlg+~generate&qft=interval%3d%228%22 ...Bing News]
  
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* [[Natural Language Processing (NLP)]]
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* [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]
 +
* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[GPT-4]] ... [[GPT-5]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 +
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 +
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 +
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 +
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 
* [http://www.craftyourcontent.com/writing-with-robots/ The Future of Writing, With Robots | Garrett Grams]
 
* [http://www.craftyourcontent.com/writing-with-robots/ The Future of Writing, With Robots | Garrett Grams]
 
* [http://software.intel.com/en-us/articles/using-natural-language-processing-for-smart-question-generation Using Natural Language Processing for Smart Question Generation | Aditya S -Intel AI Academy]
 
* [http://software.intel.com/en-us/articles/using-natural-language-processing-for-smart-question-generation Using Natural Language Processing for Smart Question Generation | Aditya S -Intel AI Academy]
 
* [http://medium.com/phrasee/neural-text-generation-generating-text-using-conditional-language-models-a37b69c7cd4b Neural text generation: How to generate text using conditional language models | Neil Yager]
 
* [http://medium.com/phrasee/neural-text-generation-generating-text-using-conditional-language-models-a37b69c7cd4b Neural text generation: How to generate text using conditional language models | Neil Yager]
 
* [http://arxiv.org/ftp/arxiv/papers/1510/1510.04420.pdf Narrative Science Systems: A Review | P. Sarao, P. Mittal, R. Kaur]
 
* [http://arxiv.org/ftp/arxiv/papers/1510/1510.04420.pdf Narrative Science Systems: A Review | P. Sarao, P. Mittal, R. Kaur]
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* [[Generative Pre-trained Transformer-2 (GPT-2)]]
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* [http://arxiv.org/abs/2004.14373 ToTTo: A Controlled Table-To-Text Generation Dataset | A. Parikh, X. Wang, S. Gehrmann, M. Faruqui, B. Dhingra, D. Yang, and D. Das - arXiv - Cornell Univarsity]
 +
** [http://ai.googleblog.com/2021/01/totto-controlled-table-to-text.html ToTTo: A Controlled Table-to-Text Generation Dataset | Ankur Parikh and Xuezhi Wang - Google AI Blog]
 +
** [http://www.marktechpost.com/2021/01/18/google-ai-introduces-totto-a-controlled-table-to-text-generation-dataset-using-novel-annotation-process/ Google AI Introduces ToTTo: A Controlled Table-to-Text Generation Dataset Using Novel Annotation Process | Asif Razzaq - Marktechpost]
 +
* [http://www.e-spirit.com/us/blog/natural_language_generation_the_future_of_content_management.html Natural Language Generation: The Future of Content Management | Heidi Unruh - e-Sprint]
 +
* [http://medium.com/@narrativesci/defined-natural-language-generation-22c28c3524e5 Defined: Natural Language Generation | Mary Grace Glascott - Narrative Science - Medium]
 +
 
 +
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. [http://en.wikipedia.org/wiki/Natural-language_generation Wikipedia]
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<img src="https://s10251.pcdn.co/wp-content/uploads/2022/11/2022-Alan-D-Thompson-AI-Bubbles-Rev-6.png" width="1000">
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[https://lifearchitect.ai/models/ Inside language models (from GPT-3 to PaLM) | Alan-D-Thompson]  ... [[PaLM]]
 +
 
 +
 
 +
[http://www.e-spirit.com/us/blog/natural_language_generation_the_future_of_content_management.html Benefits of Using Natural Language Generation (NGL):  | Heidi Unruh - e-Sprint]
 +
 
 +
* Deliver Better Digital Experiences at Scale - benefits from high-quality, personalized copy that no human author would create ad hoc or cost efficiently. This helps get better search engine visibility leading to an increase in organic traffic, while also increasing engagement and dwell time.
 +
* Reduce Spend while Driving Efficiency - content authors are relieved from repetitive, routine tasks and can focus on their other projects, with more time for creativity, strategy, and exploration. Meanwhile, the organization can increase content output without additional human resources. It can also minimize translation costs, as multiple output languages can be generated simultaneously.
 +
* Increase Content Quality - ensures that spelling, grammar, and structure are correct without the need for review and approval. It also supports the use of the corporate verbal brand as it relates to specific words, voice, and tone.
 +
 
 +
http://www.e-spirit.com/images/blog/shared-blog-images/nlg-diagram.png
  
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* Companies:
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[http://medium.com/@narrativesci/defined-natural-language-generation-22c28c3524e5 Different variations of NLG:  | Mary Grace Glascott - Narrative Science - Medium]
−
** [http://nlp.abodit.com/ Abodit Natural Language Engine for .NET]
 
−
** [http://www.arria.com/nlg-studio-google-landing-a/ Arria]
 
−
** [http://www.articleforge.com/ Article Forge]
 
−
** [http://automatedinsights.com/ Automated Insights]  [http://automatedinsights.com/wordsmith/ Wordsmith]
 
−
** [http://www.ax-semantics.com/en/home.html AX Semantics]
 
−
** [http://digitalreasoning.com/technology/ Digital Reasoning]
 
−
** [http://www.ey.com/en_gl EY] Ernst & Young Global Limited
 
−
** [[IBM]] [http://www.ibm.com/products/cognos-analytics Cognos Analytics]
 
−
** [http://www.lab-sense.io/ Labsense]
 
−
** [http://www.linguastat.com/ Linguastat]
 
−
** [[Microsoft]] [http://www.semanticmachines.com/technology Semantic Machines]
 
−
** [http://narrativescience.com/what-is-nlg/ Narrative Science]  [http://narrativescience.com/products/quill/ Quill]
 
−
** [http://www.onlyboth.com/ OnlyBoth]
 
−
** [http://outlier.ai/automated-business-analysis Outlier]
 
−
** [http://www.phrasetech.com/ PhraseTech]
 
−
** [http://www.retresco.de/en/ Retresco]
 
−
** [http://www.salesforce.com/products/einstein-analytics/overview/ Salesforce] Einstein Analytics platform
 
−
** [http://www.sapanalytics.cloud/product/ SAP] Analytics Cloud
 
−
** [http://specif.io/features/ Specifio]
 
−
** [http://www.syllabs.com/en/ Syllabs]
 
−
** [http://www.textengine.io/en-gb/ textengine.io]
 
−
** [http://textio.com/ Textio]
 
−
** [http://www.vphrase.com/phrazor/ vPhrase] Phrazor
 
−
** [http://yseop.com/ yseop]
 
  
 +
* <b>Advanced NLG:</b> communicates the way humans do — infusing intelligence and intent into the process from the very beginning. It assesses the data to identify what is important and interesting to a specific audience, then automatically transforms those insights into Intelligent Narratives — insightful communications packed with audience-relevant information, written in conversational language. Backed by a knowledge base, Advanced NLG systems understand the domain and can write contextually about a user’s business at a scale that is not possible by humans.
  
−
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. [https://en.wikipedia.org/wiki/Natural-language_generation Wikipedia]
+
* <b>Templated NLG:</b> Here, the user is responsible for writing templates, determining how to join ideas and interpreting the output. Essentially sentence building, it relies on business rules, basic calculations (ex: sum) and templates with boilerplate text to automate content. Templated systems are limited in their ability to draw from multiple data sources, perform advanced [[analytics]], achieve reusability from one project to the next and explain how it came to the story it created, with no understanding of what the user is trying to communicate or their particular domain.
  
 +
* <b>Basic NLG:</b> automatically translates data into text via [[Excel]]-like functions. An example of this would be a mail merge that restates numbers into language.
  
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[http://www.mediachange.ch/media//pdf/publications/MAPPING_THE_FIELD_OF_ALGORITHMIC_JOURNALISM_DoerrK.pdf Mapping the Field of Algorithmic Journalism | Konstantin N. Dörr]
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<img src="http://miro.medium.com/max/1073/0*G_a-Zoezdhr_GmqZ.png" width="700" height="400">
  
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http://www.researchgate.net/profile/Konstantin_Doerr/publication/282642995/figure/fig1/AS:650032931930141@1531991336422/figure-fig1_W640.jpg
 
  
 
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<youtube>QtQt1CUEE3w</youtube>
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<youtube>9TdvsdFX0O0</youtube>
 
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http://www.news-medical.net/image.axd?picture=2016%2f4%2fAnalysis_%26_Interpretation-1000.jpg
  
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== Augmented Analytics ==
 
−
[http://www.youtube.com/results?search_query=Augmented+Analytics+natural+language+semantics+semantics+machine+learning+ML Youtube search...]
 
−
[http://www.google.com/search?q=Augmented+Analytics+natural+language+semantics+semantics+machine+learning+ML ...Google search]
 
  
−
* [http://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019 | Susan Moore - Gartner]
 
−
* [http://www.informationweek.com/big-data/big-data-analytics/nlp-for-analytics-its-not-just-about-text/a/d-id/1336184 NLP for Analytics: It's Not Just About Text | Lisa Morgan - InformationWeek]
 
  
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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. [http://medium.com/analytics-for-humans/augmented-analytics-demystified-326e227ef68f Augmented Analytics Demystified | Bill Su - Medium] 
+
= Companies =
  
−
<youtube>uo_gaeN9zP0</youtube>
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* [http://nlp.abodit.com/ Abodit Natural Language Engine for .NET]
−
<youtube>gZL5p90hWPU</youtube>
+
* [http://www.arria.com/nlg-studio-google-landing-a/ Arria]
−
<youtube>P3b87zicji0</youtube>
+
* [http://www.articleforge.com/ Article Forge]
−
<youtube>MEirpCrK1cw</youtube>
+
* [http://automatedinsights.com/ Automated Insights]  [http://automatedinsights.com/wordsmith/ Wordsmith]
−
<youtube>3OvGd29Vri4</youtube>
+
* [http://www.ax-semantics.com/en/home.html AX Semantics]
−
<youtube>eixaUm4wlaM</youtube>
+
* [http://digitalreasoning.com/technology/ Digital Reasoning]
 +
* [http://www.ey.com/en_gl  EY] Ernst & Young Global Limited
 +
* [[IBM]] [http://www.ibm.com/products/cognos-analytics Cognos Analytics]
 +
* [http://www.lab-sense.io/ Labsense]
 +
* [http://www.linguastat.com/ Linguastat]
 +
* [http://www.marlabs.com/platforms/cognitive-computing-AI-ML-platform/ Marlabs] mAdvisor
 +
* [[Microsoft]] [http://www.semanticmachines.com/technology Semantic Machines]
 +
* [http://narrativescience.com/what-is-nlg/ Narrative Science]  [http://narrativescience.com/products/quill/ Quill]
 +
* [http://www.onlyboth.com/ OnlyBoth]
 +
* [http://outlier.ai/automated-business-analysis Outlier]
 +
* [http://www.phrasetech.com/ PhraseTech]
 +
* [https://www.qlik.com/us Qlik]
 +
* [http://www.retresco.de/en/ Retresco]
 +
* [http://www.salesforce.com/products/einstein-analytics/overview/ Salesforce] Einstein Analytics platform
 +
* [http://www.sapanalytics.cloud/product/ SAP] Analytics Cloud
 +
* [http://specif.io/features/ Specifio]
 +
* [http://www.syllabs.com/en/ Syllabs]
 +
* [http://www.textengine.io/en-gb/ textengine.io]
 +
* [http://textio.com/ Textio]
 +
* [http://www.vphrase.com/phrazor/ vPhrase] Phrazor
 +
* [http://yseop.com/ yseop]

Latest revision as of 20:48, 26 April 2024

YouTube ... Quora ...Google search ...Google News ...Bing News

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

Inside language models (from GPT-3 to PaLM) | Alan-D-Thompson ... PaLM


Benefits of Using Natural Language Generation (NGL): | Heidi Unruh - e-Sprint

  • Deliver Better Digital Experiences at Scale - benefits from high-quality, personalized copy that no human author would create ad hoc or cost efficiently. This helps get better search engine visibility leading to an increase in organic traffic, while also increasing engagement and dwell time.
  • Reduce Spend while Driving Efficiency - content authors are relieved from repetitive, routine tasks and can focus on their other projects, with more time for creativity, strategy, and exploration. Meanwhile, the organization can increase content output without additional human resources. It can also minimize translation costs, as multiple output languages can be generated simultaneously.
  • Increase Content Quality - ensures that spelling, grammar, and structure are correct without the need for review and approval. It also supports the use of the corporate verbal brand as it relates to specific words, voice, and tone.

nlg-diagram.png

Different variations of NLG: | Mary Grace Glascott - Narrative Science - Medium

  • Advanced NLG: communicates the way humans do — infusing intelligence and intent into the process from the very beginning. It assesses the data to identify what is important and interesting to a specific audience, then automatically transforms those insights into Intelligent Narratives — insightful communications packed with audience-relevant information, written in conversational language. Backed by a knowledge base, Advanced NLG systems understand the domain and can write contextually about a user’s business at a scale that is not possible by humans.
  • Templated NLG: Here, the user is responsible for writing templates, determining how to join ideas and interpreting the output. Essentially sentence building, it relies on business rules, basic calculations (ex: sum) and templates with boilerplate text to automate content. Templated systems are limited in their ability to draw from multiple data sources, perform advanced analytics, achieve reusability from one project to the next and explain how it came to the story it created, with no understanding of what the user is trying to communicate or their particular domain.
  • Basic NLG: automatically translates data into text via Excel-like functions. An example of this would be a mail merge that restates numbers into language.


image.axd?picture=2016%2f4%2fAnalysis_%26_Interpretation-1000.jpg


Companies

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