What Does Nlg Mean In Text? (Correct answer)

NLG

Acronym Definition
NLG No Listing
NLG Natural Language Generation
NLG Nose Landing Gear
NLG National Leisure Group

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What does NLG stand for?

  • NLG stands for National Lawyers Guild. Suggest new definition. This definition appears very frequently and is found in the following Acronym Finder categories: Organizations, NGOs, schools, universities, etc. Business, finance, etc.

Contents

What is NGL in text?

not gonna lie: used especially on the Internet for saying that you are going to admit something that makes you feel a bit uncomfortable or awkward. NGL, I kind of miss her.

Is NGL a bad word?

NGL is an abbreviation for “not gonna lie.” It’s usually used at the beginning of a sentence to indicate honesty or vulnerability. Like TBH, NGL’s tone can change depending on the context. It could be used to insult someone, to share your honest opinion, or to open up your emotions.

What do UwU mean?

Uwu is an emoticon depicting a cute face. It is used to express various warm, happy, or affectionate feelings. A closely related emoticon is owo, which can more specifically show surprise and excitement. There are many variations of uwu and owo, including and OwO, UwU, and OwU, among others.

Is UwU a flirt?

“UwU” can also be used to signify relaxation; as a way to flirt; “same”; “cool”; to be condescending; smug, or when you can’t think of a response but don’t want to be rude and not say anything. In the furry community, it can also be considered foreplay. “UwU” also became a popular song.

Is OwO furry or Weeb?

The emoticon has been popular in the furry fandom. It also has a more surprised and sometimes allusive variant, OwO (also associated with the furry fandom and often the response, “what’s this?”) that may too denote cuteness, as well as curiosity and perplexion.

What does this emoji mean?

What does Pleading Face emoji mean? It is meant to represent the typical face one makes when pleading, that is, trying to win their compassion or sympathy. Besides conveying such acts as pleading, begging, or beseeching, the Pleading Face emoji also variously conveys sadness, guilt, cuteness, and even arousal.

What does it mean when a girl types uwu?

An “uwu girl” is a girl who brings the emoticon to life through her aesthetic and her demeanor, which tends to be sweetly childlike, but also suggestive, drawing on the sexualization of (usually young) anime girls.

What does NLG Stand For? 71 meanings of NLG by Acronymsandslang.com

Rank Abbreviation Meaning
***** NLG NEL CargoICAO Aircraft Codes
**** NLG Netherlands guilderCurrencyMoney
*** NLG Nelson Lagoon Airport, Nelson Lagoon, Alaska, United StatesIata Airport Codes United StatesAlaska
** NLG Nitrogen And Light GasesChemistry
* NLG Nose Landing GearAviationNASAMilitary
* NLG North LouisianaGulfRailroad Company
* NLG MIDLOUISIANA
* NLG Nederlands Guldens
* NLG Natural Liquid Gas
* NLG Next Level Guitar
* NLG North Louisiana and Gulf Railroad Company
* NLG Near Leets Gaming
* NLG Nagios Looking Glass
* NLG Navigazione Libera del Golfo
* NLG Kansas City Southern Railway
* NLG Nerve Conduction VelocitiesNeurologyNerves
* NLG Network Leading GroupInternational business
* NLG North Louisiana and Gulf Railroad
* NLG Nashoba Learning Group
* NLG National Leisure Group
* NLG National Library of Greece
* NLG Navy Lion Group
* NLG National Liaison Group
* NLG Nonstop Laptop Guardian
* NLG new link genetics

What does NLG mean? – NLG Definitions

Are you seeking for the meanings of the letters NLG in a sentence? On the following figure, you can see some of the most important definitions for NLG. If you wish, you can also save the picture file to print it or share it with a friend via social media sites such as Facebook, Twitter, Pinterest, Google, and so on. Please scroll down to view all of the possible meanings of NLG. The whole list of definitions is provided in the table below, which is organized alphabetically.

Major Meanings of NLG

The meanings of NLG that are most frequently employed are depicted in the following figure. For offline usage, you may download the picture file in PNG format, and you can also email the image to your friends. If you are a webmaster of a non-commercial website, you are welcome to post a picture of the NLG definitions on your website for others to see.

All Definitions of NLG

As previously stated, the following table contains all of the possible meanings of the term NLG. Please keep in mind that all of the definitions are provided in the following order: alphabetical. You may view further information about each word by clicking on the links to the right, which provide definitions in both English and your native language.

Acronym Definition
NLG National Lawyers Guild
NLG National Leisure Group
NLG Natural Language Generation
NLG Nederlands Guldens
NLG Next Level Guitar
NLG No Lapse Guarantee
NLG Nonstop Laptop Guardian
NLG North Louisiana and Gulf Railroad Company
NLG Nose Landing Gear

What does NLG stand for in text

The acronym or shorthand term NLG is described in plain English as a word that stands for noun and verb. This website demonstrates how NLG may be utilized in message and chat forums, as well as social networking software such as VK, Instagram, Whatsapp, and Snapchat, among other applications. You can see all of the several definitions of NLG in the table above: some are educational words, some are medical terms, and even some computer phrases are included. If you know of another definition for NLG, please let us know so we can include it.

Please keep in mind that some of our acronyms and their explanations have been contributed by our visitors to our website.

As a thank you, we have translated the acronym NLG into other languages, including Spanish, French, Chinese, Portuguese, Russian, and others.

NLG

A prominent player in Artificial Intelligence (AI) and one of the world’s leading NLG specialists, YSEOP was founded in 2007 by Alain Kaeser on the basis of research he began at the Ecole Nationale Supérieure (ENS) Cachan. The company’s primary clients are Fortune 500 organizations. Institutions were divided into two categories: land-grant (LG) and non-land-grant (NLG), while appointments were divided into two categories: STEM-focused (STEM) and non-STEM (NonSTEM) (NSTEM). According to a news release released by the National Lawyers Guild on August 11, 2017, ” NLGMobilizes Legal Support Ahead of ‘Unite the Right’ Counter-Protests.” “The National Lawyers Guild (NLG) is arranging legal support in preparation of counter-protests to the white supremacist ‘Unite the Right Free Speech Rally,’ which is scheduled to take place tomorrow in Charlottesville, Virginia,” the statement said.

By instilling in rioters a sense of impunity and the belief that they would be exempt from the legal consequences of their illegal activities, the National Liberation Guard emboldens Antifa street fighters and ensures that the violence will continue to escalate.

This study investigates natural language generation (NLG), which is a crucial enabling technology of this trend that can offer replies to inquiries.

Virginia Beach, VA, May 06, 2013 -(PR.com)- The city of Virginia Beach is home to the University of Virginia.

PNN As part of a collaboration with the National Lawyers Guild (NLG), the Student Speech Working Group, and other organizations, the Center for Constitutional Rights (CCR) today announced the launch of the Palestine Solidarity Legal Support Initiative, which will help ensure that Palestinian rights activists have the legal support they need to exercise their First Amendment rights while speaking out and organizing.

The insurance sector adopted universal life insurance with a no-lapse guarantee in 2006.

The Coppenraths have participated in the marathon to raise funds for their daughter’s school, the Nashoba Learning Group (NLG) in Bedford, which is a nonprofit organization.

THE BANKING AND CREDIT NEWS FOR OCTOBER 4, 2011 In addition, Fitch retains NLGInsurance’s IFS at B with an outlook of “stable” (C) M2 COMMUNICATIONS ANNUAL REPORT 2011

NLP vs. NLU vs. NLG: the differences between three natural language processing concepts

The areas of natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all linked, although they are distinct from one another in terms of scope. At a high level, NLU and NLG are just components of neurolinguistic programming (NLP). They are frequently mistaken in conversation because of their intersection, but we will explain each phrase individually and highlight their distinctions in this post to clear up any confusion.

What is natural language processing?

Artificial intelligence, linguistics, and data science are all used in natural language processing, which is a branch of computational linguistics that employs methods from a wide range of fields to enable computers to interpret human language in both written and spoken form. While computational linguistics is primarily concerned with the mechanics of language, natural language processing is more concerned with the use of machine learning and deep learning approaches to fulfill tasks such as language translation or question answering.

  1. To do so, it uses methods such as tokenization, stemming, and lemmatization, which investigate the root forms of words, to identify named entities (a process known as named entity recognition) and word patterns (a process known as word pattern recognition).
  2. While there are a variety of natural language processing algorithms available, various techniques are often employed for different sorts of language jobs.
  3. Recurrent neural networks assist in the generation of the most suited text sequence.
  4. These approaches are used in conjunction to enable popular technologies such as chatbots and voice recognition systems such as Amazon’s Alexa and Apple’s Siri, among others.

What is natural language understanding?

Natural language understanding is a subset of natural language processing that determines the meaning of a phrase by analyzing the syntactic and semantic structure of text and voice. The syntax of a sentence refers to the grammatical structure of the phrase, but the semantics of the sentence relates to the intended meaning of the statement. As part of its implementation, NLU creates a relevant ontology: a data structure that describes the links among words and phrases. The combination of these analyses, which people naturally perform when conversing, are necessary for machines to discern the intended meaning of distinct texts.

Understanding the differences between homonyms and homophones is an excellent illustration of the intricacies of language. As an illustration, let us consider the following two sentences: More information about IBM Watson Natural Language Understanding may be found here.

  1. Alice is swimming in the opposite direction of the stream. The most recent version of the report is included within the folder

The term current is a noun in the first phrase, and it is used as a preposition. In addition, the verb swimming that follows it gives the reader with extra context, which allows us to deduce that we are referring to the flow of water in the ocean. In the second phrase, the term current is used as an adjective, rather than as a noun. In this case, the word “version” refers to a report that has gone through numerous revisions. This allows us to know that we are talking about the most up to current version of the file.

The use of emotional intelligence, in particular, allows companies to keep a closer eye on their customers’ feedback, allowing them to group good and negative social media comments and track net promoter ratings more effectively.

What is natural language generation?

Natural language generation is a subset of natural language processing that is also known as natural language processing. While natural language understanding is concerned with computer reading comprehension, natural language creation is concerned with the ability of computers to write themselves. In natural language generation (NLG), the process of creating a human language text response based on some data input is described. It is also possible to have this text turned into a spoken format by using text-to-speech services.

  1. Key Point Analysis, which is featured in That’s Debatable, is powered by extractive summarization, an AI invention.
  2. An NLG system would fill in the blanks, much like a game of Mad Libs, based on some data or query results.
  3. Learn more about IBM Watson Discovery by visiting their website.
  4. They approach the problem in three stages:
  • In this stage, overall content is defined and organized in an orderly fashion
  • It is also known as text planning. Sentence planning: This step takes into account punctuation and text flow, separating information into paragraphs and sentences, and integrating pronouns or conjunctions where necessary
  • It is followed by proofreading. Realization: This stage is concerned with grammatical precision, and it ensures that rules about punctuation and conjugations are adhered to in the writing. In the past tense of the verbrunisran, notrunned, for example, is used.
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NLP vs NLU vs. NLG summary

  • Aiming to convert unstructured language data into structured data format in order to enable machines to interpret voice and text and generate suitable, context-sensitive replies, natural language processing (NLP) is a branch of computer science. Natural language processing and natural language production are two of the subtopics covered in this course. Natura linguae understanding (NLU) is concerned with machine reading comprehension through grammar and context, which allows it to identify the intended meaning of a phrase. Computer-generated text, often known as natural language generation (NLG), is concerned with the creation of text in English or other languages by a machine, usually on the basis of a dataset.

Infuse your data for AI

NLP and its subsets have a wide range of practical applications in today’s society, including healthcare diagnosis and online customer service, to name a few. Take a look at some of the most recent IBM Natural Language Processing research or some of the company’s commercial offerings, such as Watson Natural Language Understanding. This service provides insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data, allowing you to respond to user demands more quickly and efficiently.

Its text analytics solution is available to all customers. Help your company get on the correct route to analyzing and infusing your data at scale for artificial intelligence.

What is Natural Language Generation?

Natural language generation (NLG) is the process of producing written or spoken narratives from a data collection via the use of artificial intelligence (AI) programming. Computational linguistics, natural language processing (NLP), and natural language understanding (NLU) are all relevant to human-to-machine and machine-to-human interaction (NLU). The majority of NLG research is devoted to the development of computer systems that offer context for data points. Numerical data mining software that is sophisticated in nature can mine vast amounts of numerical data, detect trends, and convey that knowledge in a way that is simple for humans to grasp.

When done correctly, NLG output can be published as online content in its entirety.

How NLG works

NLG is a multi-stage process, with each stage improving the data that will be utilized to create material that has a natural-sounding voice and cadence. The NLG process is divided into six steps, which are as follows:

  1. Analyze the content. In order to identify what information should be included in the final content created, data is filtered using several filters. Identification of the primary themes and links between them in the source material is part of this step. Understanding of the data. The information is evaluated, patterns are recognized, and the information is placed in context. At this point, machine learning is frequently employed
  2. Document structure is another example. Document planning is undertaken, with a narrative framework selected in accordance with the kind of data being analyzed
  3. The aggregate of sentences. In order to provide an accurate summary of the issue, relevant sentences or portions of sentences are merged in appropriate ways Grammatical structure is important. The application of grammatical principles results in the generation of natural-sounding writing. The syntactical structure of the sentence is deduced by the computer algorithm. It then makes use of this information to reconstruct the phrase in a grammatically accurate manner
  4. This is referred to as the presentation of language. The final output is created depending on a template or format that has been set by the user or programmer
  5. And

Learn about the six phases involved in the development of natural language.

How is NLG used?

Natural language generation is being employed in a wide variety of applications nowadays. The following are only a few examples of its numerous applications:

  • The responses of chatbots and voice assistants such as Google’s Alexa and Apple’s Siri
  • Converting financial reports and other types of business data into easily understood content for employees and customers
  • Automating lead nurturing email, messaging and chat responses
  • Generating and personalizing scripts used by customer service representatives
  • Aggregating and summarizing news reports
  • And reporting on opportunistic sales opportunities.

NLG vs. NLU vs. NLP

A broad term that relates to the use of computers to interpret human language in both written and spoken forms, natural language processing (NLP) may be divided into two categories. Using a system of rules and components, natural language processing (NLP) turns unstructured input into a structured data format. Natural language processing is comprised of two processes that are closely connected to one another: natural language understanding and natural language production. It includes both natural language processing (NLP) and natural language understanding (NLU), both of which have the following different, but related, capabilities:

  • The capacity of a computer to utilize syntactic and semantic analysis to discern the meaning of text or voice is referred to as NLU
  • NLG refers to the ability of computing devices to create text and speech from data input is referred to as NLG.

Chatbots and “recommended text” capabilities in email clients, such as Gmail’s Smart Compose, are examples of applications that make use of both natural language understanding and natural language generation (NLG). Natural language understanding allows a computer to comprehend the meaning of a user’s input, while natural language generation produces a text or audio response that is understandable to the user. Natural language understanding and generation are complementary technologies. NLG is interconnected with both natural language understanding and information retrieval.

Much of the fundamental research in natural language processing intersects with computational linguistics as well as with disciplines concerned with human-to-machine and machine-to-human interaction, among other things.

NLG models and methodologies

NLG creates machine-generated text in response to human inputs by utilizing machine learning algorithms and other methodologies. The following are some of the approaches that have been employed: The Markov chain is a mathematical model of probability. When it comes to statistics and machine learning, the Markov model is a mathematical framework that is used to represent and study systems that are capable of making random decisions, such as language production. When a Markov chain is started, it starts with an initial state and then randomly generates future states depending on that state.

  • Machine learning algorithms generate phrases and sentences by selecting words that are statistically probable to appear together in a given situation.
  • These artificial intelligence systems are used to process sequential data in a variety of ways.
  • RNNs are also used to find patterns in data, which may be utilized to aid in the identification of objects such as photographs.
  • Short-term memory that lasts a long time (LSTM).
  • LSTM networks are frequently utilized in natural language processing jobs because they are capable of learning the context necessary for comprehending sequences of input.
  • Transformer.
  • The Transformer has something to do with artificial intelligence.

Transformer is comprised of two encoders: one for processing inputs of any length and another for outputting the sentences that have been created by the first. The following are the three most popular Transformer models:

  • NLG technology, known as Generative Pre-trained Transformer (GPT), is a sort of NLG technology that is used in conjunction with business intelligence (BI) tools. When GPT is used in conjunction with a business intelligence system, it writes reports, presentations, and other material using NLG technology or machine learning algorithms. The system creates content depending on the information it receives, which might be a combination of data, metadata, and procedural rules
  • The system can also be programmed to generate content. Google’s Bidirectional Encoder Representations from Transformers (BERT) system is a direct descendant of the Transformer system, which was first developed for the company’s voice recognition service. It is an artificial neural network that is trained on a set of data. BERTis a language model that learns human language by learning the syntactic information (the relationships between words) and the semantic information (the meaning of the words). XLNetis an artificial neural network that learns human language by learning the syntactic information (the relationships between words). It discovers patterns, which it then employs in order to reach a logical conclusion. A natural language processing engine (NLP engine) can extract information from a basic natural language query. Text-reading and text-interpretation skills are being taught to XLNet, which will then utilize this knowledge to produce new text. XLNet is composed of two components: an encoder and a decoder. Language’s syntactic rules are used by the encoder to turn sentences into vector-based representations, and these rules are used by the decoder to convert the vector-based representation back into a meaningful sentence.

Learn more about why natural language processing (NLP) is at the forefront of AI adoption and the critical role that NLP and NLG are playing in the use of artificial intelligence in the industry. This page was last modified on July 20, 2021 EST.

Continue Reading About natural language generation (NLG)

  • NLG technologies driven by AI invigorate, personalize, optimize content
  • The use of artificial intelligence in content writing accelerates the publication process.

Dig Deeper on AI technologies

Natural language generation (NLG) is a software process that generates output in the form of natural language. There is widespread agreement that the output of any NLG process is text; however, there is significant dispute about how important it is for the inputs of an NLG system to be non-linguistic as well. The development of different reports, such as weather and patient reports; the creation of picture captions; and the creation of chatbots are all examples of NLG approaches in use. Automated NLG can be compared to the process individuals go through when putting their thoughts into writing or speaking out loud.

Translation systems for artificial computer languages, such as decompilers and transpilers, can also be likened to NLG systems since they both create human-readable code from an intermediate representation.

Natural language generation (NLG) can be thought of as a complement to natural language understanding (NLU): whereas in natural language understanding, the system must disambiguate the input sentence in order to produce the machine representation language, in NLG, the system must make decisions about how to put a representation into words (or vice versa).

When dealing with unclear or incorrect user input, NLU must deal with it, but the concepts that the system wishes to communicate through NLG are often known in advance.

However, although NLG has been around since the mid-1960s when ELIZA was established, the methodologies were only commercially available in the 1990s.

NLG may also be conducted by the use of machine learning to train a statistical model, which is often done on a huge corpus of human-written texts.

Example

ThePollen Forecast for Scotlandsystemis a straightforward example of a straightforward NLG system that might serve as a model for others. A total of six numbers are entered into this system, each of which represents projected pollen counts in various locations of Scotland. As an output, the system creates a brief written summary of pollen levels based on the input values. For example, using historical data from July 1, 2005, the program generates the following results: According to the National Weather Service, grass pollen counts for Friday have risen from moderate to high levels from the previous day, with values ranging from 6 to 7 over much of the country.

As a result of this data, a human meteorologist wrote the following forecast: “Pollen counts are anticipated to stay high at level 6 over much of Scotland, and even at level 7 in the south east.

” The only areas of respite are the Northern Isles and the far northeast of mainland Scotland, where pollen counts are at moderate levels. The comparison of these two highlights some of the decisions that NLG systems must make, which are explored in further detail below.

Stages

Making text is as simple as storing a list of canned text that can be copied and pasted, potentially with some glue text in between each piece of canned text. It is possible that the outcomes will be adequate in basic fields such as horoscope machines or generators of customized business letters. A complex NLG system, on the other hand, must contain steps of planning and merging of information in order to generate text that seems natural and does not become repetitive. According to Dale and Reiter, the normal phases of natural-language generation include the following: Determination of the content: Choosing whatever material to include in the text is the first step.

Document structure refers to the overall organization of the information that is being conveyed.

Combining comparable phrases to increase readability and naturalness is referred to as aggregate.

  • According to the National Weather Service, grass pollen levels will be between 6 and 7 in most regions of the country on Friday, up from the moderate to high levels seen yesterday.

Into the single phrase that follows, insert the following:

  • According to the National Weather Service, grass pollen levels for Friday have risen from moderate to high levels from the previous day, with values ranging from 6 to 7 in most regions of the country.

Lexical choice is the process of giving language to abstract ideas. For example, selecting whether the terms medium or moderate should be used to describe a pollen level of 4 is a difficult decision. Referring to the creation of expressions: It is possible to create referential statements that identify items and geographic places. When settling on a specific Scottish region to refer to, for example, the Northern Isles and far northeast of mainland Scotland, it is important to be specific. Making choices concerning pronouns and other sorts of anaphora is also part of this job description.

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For example, the future tense ofwill be is substituted for the present tense ofto be.

Or to put it another way, we develop an NLG system by training a machine learning algorithm (typically anLSTM) on a huge data set of input data and associated output texts (authored by humans).

Applications

NLG systems that generate jokes (seecomputational humor) have received the most attention in the popular media; however, from a commercial standpoint, the most successful NLG applications have been data-to-text systems that generate textual summaries of databases and data sets; these systems typically perform data analysis in addition to text generation. Textual summaries have been demonstrated to be more helpful for decision assistance than graphs and other visualizations, while computer-generated texts have been found to be superior (from the reader’s perspective) than human-written texts.

  1. For example, in the early 1990s, Environment Canada employed the FoG system to provide weather predictions in both French and English.
  2. The success of FoG prompted more effort, both in research and in the commercial sector.
  3. The use of NLG to summarize financial and business data is currently generating a great deal of commercial interest in the industry.
  4. Aside from automated journalism, chatbots, product descriptions for e-commerce sites, summarizing medical records and improving accessibility, NLG is also being utilized commercially in a variety of fields (for example by describing graphs and data sets to blind people).

It is an acronym that stands for “What you see is what you meant” and allows users to see and alter a constantly rendered view (NLG output) of an underlying formal language document (NLG input), allowing them to edit the formal language without having to learn the language.

Evaluation

Researchers in the field of natural language generation (NLG) must test their systems, modules, and algorithms in the same way that researchers in other scientific domains do. This is referred to as evaluation. For analyzing NLG systems, there are three fundamental strategies to consider:

  • Activity-based (extrinsic) evaluation: present the generated text to a person and assess how effectively it aids him in the performance of a certain task (or otherwise achieves its communicative goal). In the case of a medical data summarization system, for example, the system may be assessed by distributing the summaries to doctors and determining whether the summaries assist doctors in making better judgments. Given to a human, the generated text should be evaluated for its quality as well as its utility
  • This is known as human evaluations. Text comparison utilizing an automated measure such as BLEU, METEOR, ROUGE, and LEPOR
  • Comparing produced texts to texts authored by individuals using the same input data

The ultimate aim is to determine how effective NLG systems are at assisting people, which is the first of the strategies listed above. Task-based assessments, on the other hand, are time-consuming and expensive, and they might be difficult to carry out (especially if they require subjects with specialised expertise, such as doctors). As a result (as is the case in other areas of NLP), task-based assessments are the exception rather than the rule. Research is being conducted to determine how well human ratings and metrics correspond with (and predict) task-based assessments.

Initial findings indicate that human ratings perform significantly better than analytics in this area.

These findings are preliminary in nature.

See also

  • Autocompletion
  • Automated journalism
  • Automated paraphrasing
  • Markov text generators
  • Meaning-text theory
  • Generative art and literature

References

  1. Agatt and Krahmer (Gatt and Krahmer) (2018). Natural language generation: Core tasks, applications, and assessment is the subject of a survey of the state of the art. Journal of Artificial Intelligence Research, volume 61, number 61, pages 65–170. http://arXiv:1703.09902.doi: 10.1613/jair.5477.S2CID16946362
  2. Ab https://arxiv.org/abstract=1703.09902. E. Goldberg, N. Driedger, and R. Kittredge (1994). Natural-language processing for the production of weather forecasts is described in detail in the paper. abcPortet F, Reiter E, Gatt A, Hunter J, Sripada S, Freer Y, Sykes C. IEEE Expert.9(2): 45–53.doi: 10.1109/64.294135.S2CID9709337
  3. AbcPortet F, Reiter E, Gatt A, Hunter J, Sripada S, Freer Y, Sykes C. (2009). “Automatic Generation of Textual Summaries from Neonatal Intensive Care Data” is the title of the research paper (PDF). Farhadi A, Hejrati M, Sadeghi MA, Young P, Rashtchian C, Hockenmaier J, Forsyth D. Artificial Intelligence.173(7–8): 789–816.doi: 10.1016/j.artint.2008.12.002
  4. Farhadi A, Hejrati M, Sadeghi MA, Young P, Rashtchian C, Hockenmaier J, Forsyth D. (2010-09-05). Every photograph conveys a narrative: Creating words from visuals is a complex process. The European Conference on Computer Vision is taking place this year. A. AbDale and E. Reiter, Berlin and Heidelberg: Springer-Verlag. Pages 15–29
  5. AbDale, Robert
  6. Reiter, Ehud (2000). Developing methods for the creation of natural language. Ehud Reiter’s book is published by Cambridge University Press in Cambridge, United Kingdom. ISBN 978-0-521-02451-8. (2021-03-21). NLG has a long and illustrious history. Perera R, Nand P (2017). “Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature.” Archived from the source on December 12, 2021. doi: 10.4149/cai 2017 1 1.hdl:10292/10691
  7. Computing and Informatics, vol. 36, no. 1, 2017, pp. 1–32
  8. R Turner, S Sripada, E Reiter, and I Davy are among the authors (2006). Pollen forecasts can be improved by include spatial and temporal descriptions. A report on the 2006 EACL Conference
  9. “E2E NLG Challenge”
  10. “DataLabCup: Image Caption”
  11. A. Law, Y. Freer, J. Hunter, R. Logie, N. McIntosh, and J. Quinn (2005). An investigation of the effectiveness of graphical and textual representations of time series data to aid in medical decision-making in the Neonatal Intensive Care Unit. Journal of Clinical Monitoring and Computing.19(3): 183–94.doi: 10.1007/s10877-005-0879-3.PMID16244840.S2CID5569544
  12. Journal of Clinical Monitoring and Computing.19(3): 183–94 (2017). “Data-to-Text Generation Enhances Decision-Making Under Uncertainty” by D. Gkatzia, O. Lemon, and V. Reiser (2017). (PDF). “Text or Graphics?” asks the IEEE Computational Intelligence Magazine in its 12th issue, which is online at doi: 10.1109/MCI.2017.2708998.S2CID9544295
  13. 2016-12-26
  14. E. Reiter, S. Sripada, J. Hunter, J. Yu, and I. Davy
  15. (2005). “Choosing Words in Computer-Generated Weather Forecasts.” Artificial Intelligence.167(1–2): 137–69.doi:10.1016/j.artint.2005.06.006
  16. “Choosing Words in Computer-Generated Weather Forecasts.” S Sripada, N Burnett, R Turner, J Mastin, and D Evans are among those who have contributed to this work (2014). The development of a case study: NLG is satisfying the needs of the weather industry in terms of the quality and quantity of textual weather forecasts. In the Proceedings of INLG 2014
  17. “Neural Networks and Modern Business Intelligence Platforms Will Transform Data and Analytics”
  18. Dr. Harris is a medical doctor that practices in Harris, Maryland (2008). “Constructing a Large-Scale Commercial NLG System for an Emergency Medical Response System” (PDF). 5th International Natural Language Generation Conference, pp. 157–60
  19. Proceedings of the Fifth International Natural Language Generation Conference. Challenges for the Next Generation, 2009

Further reading

  • Robert Dale and Ehud Reiter are co-authors of this article (2000). Developing methods for the creation of natural language. Evans, Roger
  • Piwek, Paul
  • Cahill, Lynne
  • Cambridge, UK: Cambridge University Press, ISBN 978-0-521-02451-8
  • Evans, Roger
  • Cahill, Lynne (2002). What exactly is NLG? paper
  • Gatt, Albert
  • Krahmer, Emiel
  • INLG2002. New York, United States of America (2018). Natural Language Generation: Core Tasks, Applications, and Assessment” is a survey of the state of the art in natural language generation, including core tasks, applications, and evaluation. 65–170 in the Journal of Artificial Intelligence Research (JAIR). doi: 10.1613/jair.5477.S2CID16946362
  • Reiter, Ehud (arXiv:1703.09902.doi: 10.1613/jair.5477.S2CID16946362 (2018-01-16). “How do I find out more about NLG?”

External links

  • ACL Special Interest Group on Generation (SIGGEN)
  • SIGGEN is included in the ACL Anthology (which contains NLG research papers)
  • SIGGEN is also included in the ACL Anthology (which contains NLG research papers). This portal offers a collection of NLG materials and is maintained by the American Council on Learning (ACL). On demand access to Bateman and Zock’s “almost full” list of NLG systems, which is currently maintained as an interactive Wiki with a variety of visualisations and summary tables
  • A blog by Ehud Reiter on the subject of Natural Language Generation
  • The article describes the Coordinated Multimedia Explanation Testbed (COMET), which stands for Coordinated Multimedia Explanation for Equipment Maintenance and Repair.

What Does “NGL” Mean, and How Do You Use It?

CREATISTA/Shutterstock.com Despite the fact that NGL is popular on Reddit and Twitter, it has not attained the widespread acceptance that some other acronyms have. NGL is an abbreviation for “Not Gonna Lie,” and it’s a phrase that can still be found in many parts of the internet.

Not Gonna Lie

“Not going to lie” is an acronym meaning “not going to tell a lie.” It’s typically used at the start of a phrase to convey honesty or vulnerability, but it may be used wherever. The tone of NGL, like that of TBH, can alter depending on the situation. If you want to offend someone, offer your honest viewpoint, or open up your feelings, this is the phrase to employ. In the majority of cases, NGL is merely utilized to express one’s point of view. You may say something like, “NGL, I despise hot dogs,” or “NGL, metal straws are too difficult to clean,” among other things.

The History of NGL

The phrase “not going to lie” or “I’m not going to lie” first appeared in the English language somewhere in the last 100 years. It has traditionally been used to signify sincerity or vulnerability, despite the fact that it is frequently used as a slang term without meaning. To put it another way, individuals frequently remark “not going to lie” before or after expressing thoughts that aren’t particularly profound, damning, or vulnerable in nature. It appears that the phrase “not going to lie” evolved into NGL somewhere between 2009 and 2010.

Google Trends shows that the term NGL is at its highest point right now, indicating that more people are searching for it online than ever before.

In recent weeks, platforms such as Reddit and Twitter have seen an increase in the number of mentions about NGL, most likely due to the recent ” they had us in the first half, not going to lie ” meme, which was initiated by Apollos Hester.

How Do I Use NGL?

Fizkes/Shutterstock.com The acronym NGL, like TBH, is a straight abbreviation of a commonly used real-world expression. If you’re familiar with the phrase “I’m not going to lie” in everyday conversation, you’ll be ready to start utilizing NGL. Because the term doesn’t follow any strange grammatical rules, you can immediately apply your real-world knowledge to it without having to learn anything new. When telling a buddy that you don’t care for ketchup, you may remark, “Ketchup is terrible, NGL,” or something similar.

When it comes to online lingo that is based on real-world expressions, we at NGL are huge fans.

IN CONNECTION WITH: What Does the Phrase “TBH” Mean and How Do You Use It?

What is Natural Language Generation (NLG)?

It’s unreasonable to expect people to stop communicating via tales and language because they have done so for centuries. Harvard Business Review (HBR) – Here are the answers to the top five questions about natural language generation that people have (NLG).

1) What is Natural Language Generation?

NLG (natural language generation), a branch of artificial intelligence (AI), is a software technique that automatically translates input into plain-English text. As opposed to a human analyst, the technology can really convey a tale by composing the words and paragraphs for you, just as a human analyst would. National Geographic Learning (NLG) is one of the most rapidly increasing technologies being implemented in the workplace. There are several applications for NLG, but it is generally considered to be most successful when used to automate time-consuming data processing and reporting tasks.

2) What’s the goal of Natural Language Generation?

People have always shared ideas based on facts they have gathered. Due to the explosion of data that must be evaluated and understood, together with rising efforts to lower costs and fulfill consumer demands, the organization must discover inventive methods to stay up with the pace of change. As it turns out, a computer is capable of communicating thoughts derived from data on a massive scale and with exceptional precision. And it is capable of doing so in an exceptionally eloquent manner. Once routine analytical and communication duties are automated by a computer, productivity rises and individuals can devote their time and energy to higher-value activities.

We can describe a deep and complicated tale with a paragraph and a few bullet points, which is really short.

The notion of big data is simply too limited if all you have is a data scientist performing some type of analysis and then visually displaying the results through a dashboard. ​

3) How is NLG different than NLP?

This is how Gartner summarizes the difference between Natural Language Processing (NLP) and Natural Language Synthesis (NLS): “Whereas NLP is focused on deriving analytic insights from textual data, NLS is used to synthesize textual content by combining analytic output with contextualized narratives.” That is, NLP is a reader, but NLG is an author. When NLP systems analyze language, they are looking for patterns that indicate what concepts are being expressed. NLG systems begin with a collection of concepts that have been stored in data and convert them into a language that can be communicated.

4) What are the different variations of Natural Language Generation?

According to the Forbes article that was referenced previously, “The problem with most NLG systems is that they hard code intelligence into a template,” This results in systems that are fragile and difficult to alter, and that are unable to receive new data without requiring new coding.” When it comes to applications that demand the basic translation of data into text, templated NLG systems perform admirably.

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Intelligent NLG systems, on the other hand, are required for those who need to transmit data-driven information in a scalable manner.

In addition to declaring truths inside data, enterprise-grade NLG solutions go beyond this.

  • Relatively relevant: Identifies and articulates the most important ideas by comprehending the context of what has to be delivered
  • To be intuitive, one must be able to generate natural, conversational language that communicates difficult topics in a way that is simple to understand. Temporally accurate: Scaling data-driven communications that update whenever the underlying data changes

5) What is the future of Natural Language Generation?

Alexa, Cortana, and other intelligent personal assistants are ushering in a new era of convenience and efficiency for customers by making everyday chores easier and more effective. The workplace is catching up, with conversational interfaces that are promoting engagement between employees and with customers, increasing the standard on how these systems communicate with one another and with consumers. A recent article in the Harvard Business Review, “Bots that Can Talk Will Help Us Get More Value from Analytics,” states that “Bots that Can Talk Will Help Us Get More Value from Analytics.” Through the power of NLG, we will be able to conduct conversations with systems that have access to data about our world that will help us understand the state of our employment, our companies, our health, our homes, our families, our devices, and our communities – all in real time.” It will mean the difference between receiving a report and engaging in a dialogue with the other party.

However, the content will remain the same, but the interaction will seem more natural.”

What Is Natural Language Generation (NLG)?

It is the process of converting data into natural language, which is known as natural language generation (NLG). This is accomplished by the application of statistical approaches that evaluate enormous datasets and then utilize the results to construct phrases that seem natural. NLG may be applied in a wide range of disciplines, including journalism, marketing, financial reporting, and customer service, among others. Systems that create text from structured knowledge or information, such as databases and ontologies, are known as natural language generation systems (NLG).

  1. They have the ability to convert numbers into tales using pre-defined templates.
  2. Alternatively, the most complex algorithms are capable of generating full summaries, articles, or answers from scratch.
  3. Even though natural language generation (NLG) has been a topic of AI study for decades, the most spectacular, and usable, improvements in the technology have just lately occurred.
  4. Ten years later, academics at the University of Aberdeen were releasing papers on how to employ text and sentence planning technologies to improve text quality.
  5. Nonetheless, whether you realize it or not, you utilize NLG on a daily basis.
  6. Then, with each selection of one of its ideas, it gains more knowledge.
  7. The Associated Press used NLG to automatically generate machine-written business earnings reports for inclusion in its publications.

Today, the AP use NLG to absorb that data and then generate a narrative in seconds, allowing writers to devote their time to higher-value jobs within the business instead.

As a result, after NLG has been established, their system will be capable of producing thousands of stories on a large scale.

The model is named GPT-2, and it learnt how to write this effectively by examining eight million web pages over the course of many months.

Early trials have shown that GPT-3 can be used to generate everything from coherent blog posts to press releases to technical manuals, and that it can do it with a high degree of accuracy in many cases.

As of now, GPT-3 is still in its infancy, and the validity of the model hasn’t been thoroughly investigated.

In 2018, the first GPT model was introduced.

Only a year later, GPT-3 consumes 100 times the amount of data as its predecessor and is beginning to demonstrate extraordinary content creation skills, such as the ability to convert text into code and evaluate investment memoranda, among other things.

Marketers may use NLG to automate some narratives that are based on data if the data is appropriately formatted, as seen in the example below.

The relevant data and a well designed NLG template allow you to delegate some forms of content creation to machines while people develop other sorts of content and ramp up promotional efforts.

As of now, natural language generation systems can generate everything from email subject lines to fragments of blog posts to summaries of in-depth studies to short-form advertising copy to chatbot replies and much more.

Natural language generation (NLG) is a subfield of natural language processing (NLP), which is also a sort of artificial intelligence.

What is the difference between NLG and NLP?

Natural language generation (NLG) and natural language processing (NLP) are two methods that are frequently used in conjunction with one another in the field of artificial intelligence. Natural language generation (NLG) is the process of creating natural language writing, whereas natural language understanding (NLP) is the process of interpreting natural language. NLG may be thought of as a subset of NLP, which is why it’s beneficial to be familiar with both concepts. NLP is the phase that takes place before NLG may be used to create text that is legible by humans.

What is natural language understanding (NLU)?

Natural language understanding is another word that will be used in conjunction with NLG (NLU). Natural language understanding is a subset of natural language processing (NLP). Artificial intelligence (AI) is a branch of science that is dedicated to the creation of computing models that can decipher the meaning behind human speech. The ultimate objective of this study is to develop a system that is capable of understanding and responding to human speech in a manner that is indistinguishable from human cognition and reasoning.

NLU is the stage that comes before this, and it does so by extracting the meaning of words and the relationships that exist between them.

Then you may practice neurolinguistic programming (NLP).

Why do we use natural language generation?

What is the point of using NLG in the first place? In order to take advantage of its numerous advantages. Creating material at a large scale is made feasible by NLG. Instead of producing a report, narrative, or article by hand, a computer may assist you in creating it—while maintaining the appearance of human language. Some advanced NLG even allows auto-generated articles to have a narrative structure that makes it appear as if they were written by a person. It also makes it possible to use voice assistants and chatbots.

The most advanced NLGs are capable of predicting what you’re going to say or write next, and can then complete your sentence with a high degree of accuracy.

The use of NLG in consumer life makes it easier and less expensive to obtain answers to queries about products and services, receive assistance with orders, and address concerns with purchases.

It is now feasible to produce content at scale, automate written reports, and provide customer care using NLG in business and marketing, all of which can cut operating costs while increasing income.

What is an example of NLG?

Autocorrect is a frequent example of nonlinear grammar. Your phone’s artificial intelligence (AI) systems recognize whether human language is right or wrong, and they may then automatically produce the appropriate wording in a text message, email, or document. A variant of NLG is likely to be used by any machine system that creates language in an autonomous fashion. GPT-3 is one of the most widely utilized nonlinear grammar text generation models in use today. Generative Pre-Trained Transformer 3 (GPT-3) is a robot that was developed in 2020 and released in 2019.

  • Material generated by this method is becoming increasingly similar to text authored by humans in terms of sentence structure and paragraph structure.
  • NLG systems may generate text in a variety of formats, including short-form, long-form, and summary text.
  • Other NLG systems may generate short-form ad content (for example, for Facebook and search advertisements) or email subject lines, among other things.
  • As well as creating briefs and outlines, the system may take long-form material and extract shorter versions of the content to be used in other applications.
  • Any software application or gadget that responds to your voice when you talk to it is utilizing some type of natural language processing.
  • Sophisticated chatbots employ natural language processing (NLP) to analyze what you type and then produce answers depending on your responses.
  • Furthermore, it may entail responding to particular questions you have asked by pulling material from various places of the website and packaging it into a response for you.
  • These products are artificial intelligence-powered solutions that use NLG to automatically reply to leads via email and chat.

What are some natural language generation tools?

There are thousands of NLG tools available today, and they are utilized in a variety of consumer and commercial settings.

Marketing is our expertise at Marketing AI Institute, and we keep track of thousands of artificial intelligence marketing solutions. Here are some of the NLG marketing tools that we’ve discovered and are putting to use.

Arria

Arria utilizes NLG to extract and summarize data into understandable narratives that can be shared with others.

Automated Insights

In order to assist enterprises develop profits reports at scale, sports articles (box scores, results and so on), and data-driven storytelling, Automated Insights employs NLG as a foundation.

Clickvoyant

It is possible to significantly shorten the time it takes to extract insights from analytics and develop presentations based on those insights thanks to NLG technology, which is used by Clickvoyant.

Drift

Drift employs conversational natural language generation (NLG) to reduce friction in the purchase process through chat, email, video, and automation solutions.

Exceed.ai

Accomplish.ai utilizes artificial intelligence to connect with each and every sales prospect that enters your funnel, engaging them in human-like, two-way dialogues via email and chat.

HyperWrite

Hyperwrite is a natural language generation (NLG) application that generates phrases and paragraphs depending on prompts supplied by a person.

MarketMuse

MarketMuse is an artificial intelligence-driven assistant for developing content strategy and producing material on a regular basis. It makes use of NLG to develop summary briefings that instruct you on how to write articles that have the most possible impact, and it will even generate content for you automatically.

Pencil

For better Facebook ad performance, Pencil makes advantage of NLG to produce higher-performing advertisements.

Persado

When Persado generates marketing text and creative, they do it with an eye on creating the greatest possible message for each particular prospect across all channels.

Phrasee

Phraseeuses NLG is able to produce email subject lines that are superior to those written by humans, resulting in greater open rates.

Yseop

Yseop use NLG to automatically construct narratives from data across a variety of industries, including financial and medical report generation.

What steps should you take to get started with NLG?

If you have a smartphone or a voice assistant, you have most likely already done so. Although it is not difficult to get started with NLG in business and marketing, it does take some thought and strategy. In order to speed NLG adoption in your organization, here are some basic measures you may take.

1. Determine if you have a use case for basic NLG.

First, consider the stories you’re already telling with statistics in a manual manner. Consider “stories” to be any narratives that help you make sense of the facts. This might include reports, summaries, fact sheets, and other materials that are intended for either an external or internal audience. Do you write stories of this nature on a consistent basis? Describe how each of these tales follows a regular, repeatable framework (for example, you’re reporting on or delivering a story about the same sorts of statistics every week or month).

With the help of NLG, PR 20/20, the marketing firm that is behind the Marketing Artificial Intelligence Institute, was able to reduce the analysis and production time for Google Analytics reports by 80 percent.

It merely goes to demonstrate that taking advantage of low-hanging fruit may help your business generate value more quickly while also teaching you the fundamentals of natural language creation.

2. Look at how your data is structured.

Look first at the stories you’re currently telling with numbers, which you’ve already written down manually. Consider “stories” to be any narratives that help you make sense of the facts you’ve gathered so far. This might include reports, summaries, fact sheets, and other materials that are aimed for the public or at internal audiences, depending on the situation. Publish tales of this nature on a frequent basis do you? Describe how each of these narratives follows a regular, repeatable framework (for example, you report on or tell a story about the same sorts of data each week or month).

PR 20/20, the marketing agency that is behind the Marketing Artificial Intelligence Institute, has employed NLG to reduce the analysis and production time of Google Analytics reports by 80 percent, saving them valuable time and resources.

3. Be realistic about your ROI.

Even the most basic NLG solutions take a significant amount of time to configure and deploy. You’ll also have to pay for the solution itself, as well as any linked NLG services. To achieve this, you’ll need to take a hard look at the technology, what it can do for you, and how much you can expand your business while utilizing it. Start by determining how much time reports, articles, and narratives now require, and then determine how much time NLG might possibly save by using its technology. Finally, apply the time savings to all of the employees who might be affected by NLG.

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