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What is Natural Language Generation in Data Science, and Why Does It Matter?

What is natural language generation in data science

Many people are freaked out over artificial intelligence (AI) and worry that machines are replacing people and taking jobs. One aspect that makes some uncomfortable is how machines can produce conversations that can pass for human speech. The tool that makes that feature possible is natural language generation (or NLG for short), a primary component of generative AI. That’s what we’re here to discuss today.

This article explores natural language generation in data science. We will define the term and explain its importance, workings, stages, benefits, and the best practices you can learn in an online data science bootcamp. Then, we will round out the discussion by exploring NLG’s applications and the difference between different forms of artificial language.

So, what is natural language generation, and how does it apply to data science?

What is Natural Language Generation?

Natural Language Generation, or NLG, is a software process powered by artificial intelligence that generates natural spoken or written language from structured or unstructured data. NLG helps computers respond to users in languages that humans understand rather than how computers “talk” to each other.

NLG is a sub-group of Natural Language Processing (or NLP for short) and works with Natural Language Understanding (NLU) to generate NLP.

Natural Language Generation (NLG) is a foundational element of generative AI. AI writing tools, chatbots, voice assistants, and, yes, ChatGPT all use NLG. Any AI system that produces text humans understand is leveraging NLG to some extent.

Also Read: What is Exploratory Data Analysis? Types, Tools, Importance, etc.

How Does Natural Language Generation Work?

The NLG process begins with three crucial AI components working together. We’ve already mentioned some of them in passing. They are:

  • Language Models. Language models are the AI “brain” trained on vast amounts of text you input your data or prompt into. These models learn nuance and patterns, letting them generate text that sounds like a person.
  • Natural Language Processing (NLP). Consider NLP as the machine’s ability to read. Natural Language Processing refers to the machine’s ability to break down and comprehend commands, prompts, and the provided data.
  • Natural Language Understanding (NLU). NLU focuses on comprehension. The term means the machine analyzes relationships and meanings within the data to ensure the resulting generated text makes sense and is accurate.

Using these components, Natural Language Generation produces text by employing the following processes:

  • Data Input: The NLG system receives the structured data (e.g., database entries, prompts, and spreadsheets).
  • NLP Analysis: Natural Language Processing breaks down the data, identifies the speech parts, and analyzes syntaxes.
  • NLU Interpretation: Natural Language Understanding determines the relationships and meaning within the data, guiding the text generation process.
  • Content Planning: The Natural Language Generation system decides what information to include and how to structure that information into sentences and paragraphs.
  • Text Generation: The language model produces the final output, producing human-readable text based on data and insights gained from NLP and NLU.

The Stages of Natural Language Generation

Natural Language Generation is typically broken down into six steps:

  • Data analysis. First, data (structured data such as financial information and unstructured data like audio call transcriptions) must be analyzed. This data is filtered to ensure that the final generated text is relevant to a user’s needs, whether to generate a specific report or answer a query. At this stage, the NLG tools will select the main topics in the source data and the relationships that exist between each topic.
  • Data understanding. This is the step where Natural Language processing, machine learning, and a language model enter the picture. The software identifies data patterns and, based on algorithmic training, can interpret what’s being said and the context of the statements. For numerical or non-textual data, the software locates the data it’s been taught to recognize and can comprehend its relationship to the actual text.
  • Document creation and structuring. At this point, the Natural Language Generation solutions are working to produce data-driven narratives derived from the data being analyzed and requested results (e.g., a report, chat response, etc.). Finally, a subsequent document plan is created.
  • Sentence aggregation. Sentences and sentence fragments labeled as relevant are assembled to summarize the presented information.
  • Grammatical structuring. The software begins the generated text, employing natural language grammatical rules to make the text accommodate human understanding.
  • Language presentation. The software creates the final output in the user’s chosen format in the final step. As mentioned earlier, this form could be a report, a voice assistant response, or a customer-targeted e-mail.

Also Read: What is Data Wrangling? Importance, Tools, and More

The Different Techniques Used for Evaluating NLG Systems

There are three distinct techniques used to evaluate Natural Language Generation systems.

  1. Human ratings. Human ratings assess the generated text based on ratings provided by a human that measure the text’s quality and usefulness.
  2. Metrics. Metrics compares the generated texts to texts written by human professionals.
  3. Task-based evaluations. These evaluations cover human-based evaluation. In this case, humans assess how well the NLG helps perform a task. For instance, a system that generates medical data summaries can be evaluated by providing doctors with these summaries and assessing whether the summaries will help those doctors make better decisions.

The Advantages of Using Natural Language Generation

Natural language generation has a lot to offer, such as:

  • It can speed up the analysis of vital data. Organizations can use NLG software to quickly scan large quantities of input and generate reports instead of analyzing critical business information manually or spending vast amounts of time examining complex underlying data. For example, instead of studying vast structured data streams in a business database, you can set the NLG tool to develop a narrative structure in a language the team can easily understand. Also, you can make it easier for users to frame the inquiries to your software in the syntax they usually use and then get a quick, easily understandable response. This process saves time, money, and the resources necessary to analyze data.
  • It can respond to input quickly on the company’s behalf. Depending on the size and type of your business, you might need to generate thousands of speech or text-based outputs that could otherwise be automatically generated using Natural Language Generation. Content creation examples include:
    • Automatic responses to surveys
    • Chatbot or voice assistant replies
    • Customer e-mails
    • Product descriptions
    • Sales reports

Using NLG, you can better take on the otherwise dull task of creating these individually. This process reduces the effort, resources, and time needed to respond to these queries manually, thus reducing costs in offering people superior customer service.

  • It can help improve customer relationships. Thanks to Natural Language Generation, you can summarize millions of customer interactions and tailor them to specific use cases. Even better, you can frame automatic responses in a more human-like way that adjusts to what’s being said. Customers want to feel like they’re being listened to, not treated like a cold transaction. Even if the responses are being made by something like NLG, people are good with it if it sounds and feels human. You can considerably strengthen customer relationships by using NLG techniques to create personalized responses to customer comments.

Natural Language Generation Best Practices

Two practices must be part of any successful Natural Language Generation process.

  1. Select a highly intelligent system to transform your business internally. Depending on all the teams in every department in your organization to analyze every byte of data gathered is not just time-consuming but also inefficient. Pull that burden off your employees and begin automatically generating key insights using NLG tools to create reports and respond to customer input via automatic responses and reports. An integrated system lets multiple teams keep abreast of the most up-to-date, in-depth insights and automatically initiate responsive actions.
  2. Use Artificial Intelligence to your advantage when dealing with customer responses. Customers are good at constantly offering feedback, whether it’s through surveys, third-party reviews, social media comments, or other forums. Most people with whom your company interacts want to form connections with your business. By using NLG techniques to respond intelligently and quickly to those customers, your business reduces the time customers spend waiting for a response, reducing your cost to serve these folks. As a result, they feel like they matter and consider themselves better connected and heard. So, don’t leave your customers waiting, and don’t miss the opportunity to have vast amounts of customer data available to create more impactful insights.

Common Applications of Natural Language Generation

  • Automated Reporting. NLG converts raw data into clear, insightful reports, saving data analysts valuable work time. Analytics platforms equipped with Natural Language Generation can even break down complex data into more straightforward concepts, making the findings accessible and understandable to non-technical stakeholders.
  • Chatbots and Virtual Assistants. NLG-powered conversational Artificial Intelligence systems answer everyday questions, troubleshoot problems, and take orders 24/7, thus freeing up your human staff to handle the more complex issues that AI can’t work with.
  • Content Creation. NLG tools can generate anything, from basic social media posts and product descriptions to summaries and even full-length feature articles.
  • Hyper-Personalization. Customers appreciate a personal touch. So, NLG can tailor content and recommendations based on the user’s preferences and past behavior, resulting in a more engaging experience.
  • Machine Translation. NLG software allows real-time translation, making documents, websites, and conversations accessible to a worldwide audience, regardless of what language the audience speaks.
  • Sentiment Analysis. NLG helps marketers analyze emotions expressed in language, letting them better understand how their customers feel about the brand, products, or services.
  • Voice Assistants. Popular voice assistants like Alexa and Siri employ Natural Language Generation to understand people’s requests, supply information, and control smart home devices.

Also Read: What is Spatial Data Science? Definition, Applications, Careers & More

What Are the Differences Between NLP, NLG, and NLU?

Natural language generation employs AI to translate data into speech or text. On the other hand, it provides NLG with that data. NLP is the act of accurately changing what people say into machine-readable data so that natural language generation can then use that data to generate a response.

Remember, to develop a response, the machine must “understand” the prompt or conversation. So, to put it in very simple, straightforward terms, NLP reads or hears, while NLG writes or speaks.

So, we now know that NLP translates what people say into data, and the NLG system uses that data to generate language that a human can understand. Great! But what if the machine’s answer doesn’t make sense? That’s why we have Natural Language Understanding (or NLU).

Natural Language Understanding is Artificial Intelligence that employs computational models to interpret the meanings behind human language. NLU analyzes data produced by NLP to comprehend the meaning of a person’s words and the relationships between concepts.

So, NLG generates language that reads or sounds human, and NLU ensures that the human-sounding language makes sense and means something. As a result, if the NLU works properly, people will get a response from a voice assistant or chatbot that makes perfect sense.

Do You Want Data Science Training?

When discussing terms like AI, NLG, NLP, or NLU, you discuss concepts with solid data science roots. If you have strong data science skills, you can better grasp and work with these fantastic forms of technology. Try this online data science program if that sounds like a good idea.

This 44-week online bootcamp teaches you data science and generative AI skills through a high-engagement learning experience. You will learn concepts such as Generative AI, Prompt Engineering, ChatGPT, DALL-E, Midjourney, and other popular tools.

Data science is a fundamental part of NLG and other associated concepts, and it is a field that pays well. Indeed.com reports that data scientists earn an average annual salary of $124,215.

Check out this intense online learning experience and get the skills necessary for a successful career in today’s AI-dominated market.

FAQ

Q: What is natural language generation?
A: Natural Language Generation, or NLG, is a software process powered by artificial intelligence that generates natural spoken or written language from structured or unstructured data. NLG helps computers respond to users in languages that humans understand rather than how computers “talk” to each other.

Q: What is an example of NLG?
A: Chatbots, voice assistants, content creation.

Q: Is natural language generation generative AI?
A: Yes, NLG is a fundamental part of generative AI.

Q: What is the difference between the terms NLG and NLP?
A: NLG uses AI to translate data into text or speech. NLP supplies NLG with that data. NLP reads or hears; NLG writes or speaks.

You might also like to read:

Data Science and Marketing: Transforming Strategies and Enhancing Engagement

An Introduction to Natural Language Processing in Data Science

Why Use Python for Data Science?

A Beginner’s Guide to the Data Science Process

What Is Data Mining? A Beginner’s Guide

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