What is Natural Language Generation?
Natural text Generation, or NLG, is an artificial intelligence powered software process that uses both organized and unstructured data to generate natural written or spoken text. Instead than feeding back to people in a way that a computer might understand, it helps computers to do so in human language.
For instance, NLG can be used to provide a personalized, understandable response after analyzing client input. This enables chatbots and voice assistants to respond in a way that appears human.
Additionally, it can be used to convert complex data, including numerical data input, into reports that are simple to comprehend. NLG could be used, for instance, to automatically create weather updates or financial reports.
How does it work?
A variety of computer science procedures make it possible for Natural Language Generation technology to function. These consist of:
Linguistic computation
The study of spoken and written language from a scientific standpoint using computer-based analysis. This entails dissecting spoken or written communication and developing an understanding system that computer software may utilize. In order to develop a language model system that computers may utilize to precisely analyze human speech, it makes use of grammatical and semantic frameworks.
Processing Natural Language (NLP)
The practical application of computer linguistics to spoken or written human language is known as natural language processing, or NLP. NLG is categorized as a Natural Language Processing subcategory.
Natural Language Understanding (NLU)
Natural Language Understanding (NLU) attempts to ascertain the speaker’s mood, meaning, effort, or purpose in addition to the words or phrases being used. It advances the comprehension and brings the analysis closer to what a human would comprehend from the words. In order to assist make comprehension even more detailed, Natural Language Understanding advances machine learning.
Why is Natural Language Generation important for business?
Numerous business tools already employ NLG approaches, which are probably seen on a daily basis. When using search engines’ voice search feature or daily sports reporting in the news, you may observe it in action.
Consider implementing Natural Language Generation technology in your company for the following reasons:
It can expedite your critical data analysis
With NLG software, you may swiftly scan vast amounts of input and produce reports instead of manually analyzing crucial business information or by looking at the underlying data.
For instance, you can configure your NLG tools to generate a narrative framework in a language that your team can comprehend, as opposed to analyzing the vast amounts of structured data present in company databases. Additionally, you may make it simpler for consumers to ask questions about your program using the language they often use and receive a prompt, easily understandable response.
You can save time, money, and the resources required for data analysis by doing this.
It can react to input on your behalf in a timely manner
Thousands of text or speech-based outputs could be produced automatically utilizing Natural Language Generation (NLG), depending on your line of work. Examples of content creation could be:
- Descriptions of products
- Reports on sales
- Emails from customers
- Survey responses that are automatically generated
- Responses from voice assistants or chatbots
You can take on the difficult chore of making these one at a time by using NLG. This lowers your cost to serve by lowering the time and effort your team must expend manually answering questions.
It can help you improve your customer relationships
Millions of client interactions can be summarized using Natural Language Generation and customized for certain use cases. Even better, you can react in a way that is more human-like and especially relevant to what is being stated.
More than 60% of customers say organizations should care more about their thoughts, and two-thirds think they should listen more. You may improve your customer interactions at scale by employing NLG approaches to generate tailored responses to what your customers are saying to you.
What does using Natural Language Generation entail?
Extractive and abstractive natural language creation are the most common methods.
In order to create a summary of a lengthy text, an extractive technique selects phrases that best illustrate important ideas and integrates them in a grammatically sound manner.
By identifying important concepts and then coming up with fresh language that aims to understandably convey the main ideas of a larger body of material, an abstractive technique produces novel content.
Regardless of the method, Natural language generation entails a number of stages to comprehend human language, analyze it for insights, and produce text that is responsive.
Natural Language Generation Definition
Natural Language Generation (NLG) creates human-language text using AI and computational linguistics. It entails turning numbers, symbols, and categorical data into understandable text or speech. Analyzing incoming data and using linguistic rules and statistical models, NLG systems can generate sentences, paragraphs, or entire narratives.
Chatbots, content automation, report generating, and tailored messaging are common. NLG can improve user experiences by offering digestible information, automating text generation, and making data insights more accessible.
Natural Language Generation Tools
Natural Language Generation in six steps
Analysis of data
Data must first be analyzed, including unstructured data like call audio transcriptions and structured data like financial information. To ensure that the final text produced is pertinent to the user’s needs whether those needs are to generate a particular report or provide an answer to a query the data is filtered. Your source data’s primary themes and the connections between them will now be identified by your NLG tools.
Understanding data
This is where a language model, machine learning, and natural language processing are useful. Based on its algorithmic training, your software can evaluate what is being and the context of these remarks after spotting patterns in the data. Your program recognizes the data it has been trained to recognize and can comprehend how it connects to real text when it comes to numerical data or other non-textual data kinds.
Creating and organizing documents
Based on the data being analyzed and the outcome you have requested (report, chat answer, etc.), your NLG solutions are currently working to provide data-driven narratives. A plan for the next document is made.
Sentence grouping
The material to be given is summarized using sentences and sentence fragments that have been determined to be pertinent.
Grammatical organization
When your software generates text, it applies grammatical principles from natural language to ensure that the text is comprehensible.
Presentation of the language
Lastly, the program will produce the finished product in the format of the user’s choice. As previously said, this could take the kind of a voice assistant response, a report, or an email sent to the customer.
Natural Language Generation best practice
Systems that use Natural language generation can produce text for a variety of business applications. To make sure you’re boosting productivity and making a return on investment, it’s best to use the system strategically, just like you would with any other.
Use artificial intelligence to your advantage for responding to customers
You get feedback from your clients all the time. The individuals you engage with want to establish a relationship with your company, whether that be through surveys, reviews from third parties, comments on social media, or other channels.
Customers will spend less time waiting for a response, you will save money on serving them, and they will feel more heard and connected if you use NLG tactics to answer to them promptly and intelligently. Don’t let customers wait and don’t pass up the vast amounts of client data that can be analyzed.
Select a highly intelligent system to change your company’s internal operations.
It is inefficient and time-consuming to rely on teams from every department to analyze all of the data you collect. With NLG solutions that generate reports and automatically respond to client interaction, you can relieve your staff of some of their workload and begin producing important insights automatically. You can immediately initiate responsive actions and keep numerous teams informed about the most recent in-depth insights using an integrated system.
How to start using Natural Language Generation (NLG) Systems
Because it can be more difficult for a machine to extract the most important information from lengthy text, unstructured data can present numerous difficulties for Natural Language Generation (NLG).
In order to achieve more relevant and human-like narrative around unstructured data, they at Qualtrics adopt a more hands-on and prescriptive approach.
Qualtrics Discover offers the perfect balance of interpretability and relevancy that is customized to your company needs by combining extractive and abstractive methodologies into a hybrid-based approach. This can be applied to improve employee performance, summarize insights, and change your contact center responses.
