Natural Language Generation: What Is It?
Natural text generation, or NLG, is a software process driven by artificial intelligence that creates natural written or spoken text from both structured and unstructured input. It enables computers communicate in human language rather than in a fashion that a computer might comprehend.For example, NLG may be used to analyze client input and then offer a customized, intelligible answer. This makes it possible for voice assistants and chatbots to react in a manner that seems human.
It may also be used to create easily understood reports from complicated data, including numerical data input. For example, NLG might be used to generate financial reports or weather updates automatically.
How does it operate?
Natural Language Generation technology is made feasible by a number of computer science processes. These include:Linguistic computation
the scientific study of written and spoken language using computer-based analysis. This means breaking down written or spoken communication and creating a system of understanding that computer programs can use. It uses grammatical and semantic frameworks to create a language model system that computers can use to accurately analyze human speech.Natural Language Processing (NLP)
Natural language processing, or NLP, is the practical application of computer linguistics to written or spoken human language. NLG is classified as a subclass of Natural Language Processing.Natural Language Understanding (NLU)
In addition to the words or phrases being spoken, Natural Language Understanding (NLU) aims to determine the speaker's attitude, meaning, effort, or intent. It improves understanding and approaches the analysis more closely to what a person would understand from the text. Natural Language Understanding enhances machine learning to help make understanding even more thorough.Why does business need natural language generation?
NLG techniques are already used in many business products and are likely used on a regular basis. You could see it in action when you use the voice search function on search engines or when you watch the news's daily sports coverage.For the following reasons, think about integrating Natural Language Generation technology into your business:
It may speed up your analysis of important facts.
Instead of manually evaluating important business information or by looking at the underlying data, you can quickly scan large volumes of input and generate reports using NLG software.
Instead of examining the enormous volumes of structured data found in corporate systems, you may set up your NLG tools to provide a narrative framework in a language that your team can understand. Furthermore, you might make it easier for customers to ask questions about your program in the language they often use and get a quick, clear answer.
By doing this, you may reduce the amount of time, money, and resources needed for data analysis.
By doing this, you may reduce the amount of time, money, and resources needed for data analysis.
It can quickly respond to input on your behalf.
Depending on your field, Natural Language Generation (NLG) might automatically generate thousands of text or speech-based outputs. Some instances of content production include:
- Product descriptions
- Sales reports
- Customer emails
- Automatically generated survey replies
- Voice assistant or chatbot responses
It might assist you in strengthening your connections with customers
Natural language generation may be used to summarize and tailor millions of customer interactions to specific use cases. Better still, you may respond in a more relatable manner that is particularly pertinent to the statement.
Two-thirds of consumers believe that businesses should listen more to them, and over 60% believe that they should care more about their opinions. By using NLG techniques to provide customized replies to what your consumers are saying to you, you may enhance your customer interactions at scale.
Two-thirds of consumers believe that businesses should listen more to them, and over 60% believe that they should care more about their opinions. By using NLG techniques to provide customized replies to what your consumers are saying to you, you may enhance your customer interactions at scale.
What is involved in utilizing Natural Language Generation?
The two most popular techniques for creating natural language are extractive and abstractive.
An extraction approach chooses sentences that best convey key concepts and incorporates them in a grammatically correct way to provide a summary of a long text.
An abstractive approach creates unique content by first identifying key concepts and then developing new language that attempts to communicate the essential ideas of a larger body of information in an understandable manner.
Whatever the approach, natural language production involves many steps to understand human language, extract insights from it, and create responsive text.
Common features include chatbots, content automation, report generation, and customized messaging. By providing easily assimilated information, automating text production, and facilitating the accessibility of data insights, NLG may enhance user experiences.
If you employ NLG strategies to respond to customers quickly and intelligently, they will feel more heard and connected, you will save money on servicing them, and they will spend less time waiting for a response. Avoid making consumers wait and take use of the abundance of client data available for analysis.
At Qualtrics, they use a more hands-on and prescriptive approach to create a more meaningful and human-like story around unstructured data.
By integrating extractive and abstractive approaches into a hybrid-based approach, Qualtrics Discover provides the ideal ratio of interpretability and relevance that is tailored to your business's requirements. This may be used to modify your contact center replies, summarize findings, and enhance staff performance.
An extraction approach chooses sentences that best convey key concepts and incorporates them in a grammatically correct way to provide a summary of a long text.
An abstractive approach creates unique content by first identifying key concepts and then developing new language that attempts to communicate the essential ideas of a larger body of information in an understandable manner.
Whatever the approach, natural language production involves many steps to understand human language, extract insights from it, and create responsive text.
Definition of Natural Language Generation
Natural Language Generation (NLG) uses computational linguistics and artificial intelligence to produce text in human languages. It involves converting categorical data, numbers, and symbols into comprehensible speech or writing. Through the use of statistical models and linguistic norms, NLG systems are able to analyze incoming data and produce phrases, paragraphs, or complete stories.Common features include chatbots, content automation, report generation, and customized messaging. By providing easily assimilated information, automating text production, and facilitating the accessibility of data insights, NLG may enhance user experiences.
Tools for Generating Natural Language
Six Steps to Natural Language Generation
Data analysis
All data, including structured data like financial information and unstructured data like call audio transcriptions, must first be examined. The data is filtered to make sure the final text generated is relevant to the user's requirements, whether those needs are to create a specific report or offer a response to a question. Your NLG tools will now identify the main themes in your source data and the relationships between them.Comprehending data
Natural language processing, machine learning, and language models may be helpful in this situation. After identifying patterns in the data, your program may analyze what is being said and the context of these comments based on its algorithmic training. When it comes to numerical data or other non-textual data types, your software can identify the data it has been taught to recognize and understand how it relates to actual text.Making and arranging papers
The data-driven narratives that your NLG solutions are now producing are dependent on the data being analyzed and the desired result (report, chat response, etc.). A strategy is developed for the next paper.Grouping sentences
Sentences and phrase fragments deemed relevant are used to summarize the content to be presented.Grammar structure
To make sure the material is understandable, your program uses grammatical rules from natural language when it creates text.Language presentation
Finally, the application will generate the final output in the format of the user's selection. As mentioned before, the consumer may get an email, a report, or a voice assistant answer.Best practices for Natural Language Generation
Natural language generation systems may generate text for a range of commercial uses. Like any other technology, it's vital to utilize it carefully to ensure that you're increasing productivity and earning a return on investment.Take use of artificial intelligence to reply to consumers
Your customers provide you with comments on a regular basis. Through surveys, third-party reviews, social media comments, or other avenues, the people you interact with want to build a connection with your business.If you employ NLG strategies to respond to customers quickly and intelligently, they will feel more heard and connected, you will save money on servicing them, and they will spend less time waiting for a response. Avoid making consumers wait and take use of the abundance of client data available for analysis.
To alter the internal processes of your business, use a highly intelligent system
Relying on teams from each department to evaluate all of the data you get is ineffective and time-consuming. You may reduce the effort of your employees and start generating valuable insights automatically with NLG systems that create reports and react to client interactions automatically. With an integrated system, you may start responsive activities right away and tell several teams about the latest in-depth findings.How to begin utilizing systems that generate natural language (NLG)
Unstructured data may provide several challenges for Natural Language Generation (NLG) as it might be more challenging for a computer to extract the most crucial information from long text.At Qualtrics, they use a more hands-on and prescriptive approach to create a more meaningful and human-like story around unstructured data.
By integrating extractive and abstractive approaches into a hybrid-based approach, Qualtrics Discover provides the ideal ratio of interpretability and relevance that is tailored to your business's requirements. This may be used to modify your contact center replies, summarize findings, and enhance staff performance.
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