What is Natural Language Understanding (NLU) and how is it used in practice?

· Thomas Wood
What is Natural Language Understanding (NLU) and how is it used in practice?

Unlock Your Future in NLP!

Dive into the world of Natural Language Processing! Explore cutting-edge NLP roles that match your skills and passions.

Explore NLP Jobs

Natural Language Understanding (NLU): Overview

If we think about it, language is one of the most powerful tools in our arsenal. We use it to express how we feel or what we’re thinking. We can use it to get our point across or persuade someone or even make them laugh or cry. But what if we could use language in a way that could make us understand what someone else was thinking?

Natural language understanding in AI is the future because we already know that computers are capable of doing amazing things, although they still have quite a way to go in terms of understanding what people are saying. Computers don’t have brains, after all, so they can’t think, learn or, for example, dream the way people do.

Natural language understanding AI aims to change that, making it easier for computers to understand the way people talk. With NLU or natural language understanding, the possibilities are very exciting and the way it can be used in practice is something this article discusses at length.

What is Natural Language Understanding? A more in-depth look

Natural language understanding (NLU) refers to a computer’s ability to understand or interpret human language. Once computers learn AI-based natural language understanding, they can serve a variety of purposes, such as voice assistants, chatbots, and automated translation, to name a few.

However, the most basic application of natural language understanding is parsing, where text written in natural language is converted into a structured format so that computers can make sense of it in order to execute the desired task(s).

For instance, “hello world” would be converted via NLU or natural language understanding into nouns and verbs and “I am happy” would be split into “I am” and “happy”, for the computer to understand.

Parsing is merely a small aspect of natural language understanding in AI – other, more complex tasks include semantic role labelling, entity recognition, and sentiment analysis.

To further grasp “what is natural language understanding”, we must briefly understand both NLP (natural language processing) and NLG (natural language generation).

Natural language understanding (NLU) is where you take an input text string and analyse what it means. This can be done on a relatively small scale to begin with. For instance, when a person reads someone’s question on Twitter and responds with an answer accordingly (small scale) or when Google parses thousands to millions of documents to understand what they are about (large scale).

NLP is a process where human-readable text is converted into computer-readable data. Today, it is utilised in everything from chatbots to search engines, understanding user queries quickly and outputting answers based on the questions or queries those users type.

NLG is a process whereby computer-readable data is turned into human-readable data, so it’s the opposite of NLP, in a way. For instance, if you want to program a bot to respond to your statements or questions just as a real person would, you can use NLG software to make sure that the responses being typed look as if they are coming from an actual person, and not a computer or ‘bot’ outputting random words.

At times, NLU is used in conjunction with NLP, ML (machine learning) and NLG to produce some very powerful, customised solutions for businesses.

What is Natural Language Understanding (NLU) and how is it used in practice

NLU (Natural Language Understanding): How does it work and why is it so important?

If we were to explain it in layman’s terms or a rather basic way, NLU is where a natural language input is taken, such as a sentence or paragraph, and then processed to produce an intelligent output. You’ll typically see natural language understanding (NLU) use cases in consumer-facing applications – for example, chatbots and web search engines – where users interact with the bot or search engine using plain English or their native language.

From a relatively basic perspective, NLU (natural language understanding) can be understood by breaking it down into three stages:

Tokenisation: The first stage involves splitting the input into separate words or tokens. This includes punctuations and other symbols as well as words from all major languages.

Lexical analysis: The tokens are placed into a digital dictionary which includes how they are used in speech – for instance, whether they are to be used as nouns or verbs. The dictionary also includes identifying phrases which are to be placed in a dedicated database for referencing later.

Syntactic analysis: Those tokens are then analysed for their grammatical structure. This includes identifying the role each word plays and if any ambiguity exists between multiple interpretations of the said roles.

From the rather ‘basic’ explanations and examples above, it may still seem like natural language understanding in AI may not be that big of a deal, especially compared to, say, natural language processing or machine learning and artificial intelligence in their own – but it would be a mistake to assume that.

On the contrary, natural language understanding (NLU) is becoming highly critical in business across nearly every sector. There’s a lot businesses can do when they have software which can identify the meaning of a specific text – so much so that it can offer a serious competitive advantage by providing insights into your business data which you simply didn’t have before.

This alone should make you sit up and take notice.

You see, when you analyse data using NLU or natural language understanding software, you can find new, more practical, and more cost-effective ways to make business decisions – based on the data you just unlocked.

Let’s say, you’re an online retailer who has data on what your audience typically buys and when they buy. Using AI-powered natural language understanding, you can spot specific patterns in your audience’s behaviour, which means you can immediately fine-tune your selling strategy and offers to increase your sales in the immediate future.

Organisations, irrespective of their scale or sector, can also use natural language understanding and AI in, say, marketing campaigns when they target specific audience groups using different messaging – based on what those groups are already interested in. The natural language understanding in AI systems can even predict what those groups may want to buy next. This can offer powerful competitive advantages, as one might imagine.

Before we discuss some of the specific use cases, it’s worth understanding in depth as to why every modern and future-facing business should know the importance of natural language understanding:

Human language is rather complicated for computers to grasp, and that’s understandable. We don’t really think much of it every time we speak but human language is fluid, seamless, complex and full of nuances. What’s interesting is that two people may read a passage and have completely different interpretations based on their own understanding, values, philosophies, mindset, etc.

If people can have different interpretations of the same language due to specific congenital linguistic challenges, then you can bet machines will also struggle when they come across unstructured data.

Now, where NLU or natural language understanding really shines is that it can help to equip different types of technology with a level of understanding that’s very similar to humans – even including detecting parsing typing errors and incorrect naming. Therefore, NLU can be used for anything from internal/external email responses and chatbot discussions to social media comments, voice assistants, IVR systems for calls and internet search queries.

There’s now a more growing need for computers to understand at scale – NLU is dedicated to devising strategies and methods for understanding context in individual text, statements, or records, and that understanding needs to be at scale. Natural language understanding in AI systems today are empowering analysts to distil massive volumes of unstructured data or text into coherent groups, and all this can be done without the need to read them individually. This is extremely useful for resolving tasks like topic modelling, machine translation, content analysis, and question-answering at volumes which simply would not be possible to resolve using human intervention alone.

Business are finding this very useful, because given the sheer volume of unstructured text that each business generates on a daily basis, NLU can help you squeeze the most insights out of that text, thus, saving you plenty of money, time and energy in the process. What’s more, you’ll be better positioned to respond to the ever-changing needs of your audience.

Natural language understanding (NLU) is already being used by thousands to millions of businesses as well as consumers. Experts predict that the NLP market will be worth more than $43b by 2025, which is a jump in 14 times its value from 2017. Millions of organisations are already using AI-based natural language understanding to analyse human input and gain more actionable insights.

According to a global overview report released by DataReportal in 2022, there were 4.62 billion social media users, 4.95 billion internet users worldwide and more than two-thirds of the world’s population using mobile – all of them will experience NLU and expect some kind of AI-based natural language understanding responses when they communicate with their favourite brands or businesses.

Furthermore, consumers are now more accustomed to getting a specific and more sophisticated response to their unique input or query – no wonder 20% of Google search queries are now done via voice. No matter how you look at it, without using NLU tools in some form or the other, you are severely limiting the level and quality of customer experience you can offer.

The modern customer today expects to be heard as an “individual”. Gone are the days of generic responses and ‘one size fits all’ solutions. Today’s customers are very demanding and expect a personalised experience. In one research report, more than half of consumers thought that businesses needed to tend to their needs better, and were far more likely to buy from them both in the present and future, if they knew that the business cared – keyword being “care” in this case because it’s one thing to adequately meet your customers' expectations, and another to go the extra mile to offer them a personalised experience.

Accenture, a well-known professional services company, has reported that 91% of consumers will be more likely to buy from businesses that provide offers and/or recommendations which specifically cater to their needs and preferences. But how do you meet the needs of each customer on an individual level, especially when your target group is so vast?

This is where the power of natural language understanding (AI and ML) shines yet again – instead of relying on human resources to provide a tailored experience to each customer, you can use NLU/natural language understanding software to capture, process and react to massive quantities of unstructured data which your customers provide on an extensive scale each day. The more the NLU system interacts with your customers, the more tailored its responses become, thus, offering a personalised and unique experience to each customer.

AI and natural language understanding combined can help you cut costs and internal efforts, thus, freeing up resources to redirect toward other tasks. Let’s take a real use case to demonstrate how NLU-based technology can reduce call centre costs as well as improve customer service delivery and satisfaction levels;

Your NLU software takes a statistical sample of recorded calls and performs speech recognition after transcribing the calls to text via MT (machine translation). The NLU-based text analysis links specific speech patterns to both negative emotions and high effort levels. This is important because through the use of predictive modelling algorithms, we can identify those negative speech patterns automatically (when customers complain e.g.) in subsequent calls, and then recommend the appropriate response to the customer service rep as they are taking the call.

Imagine how much cost reduction can be had in the form of shorter calls and improved customer feedback as well as satisfaction levels.

This is merely one example of how NLU or natural language processing may be used to improve business processes and save money. As promised, here’s a look at how natural language understanding in AI may be used in practice:

How AI in natural language understanding may be used in day-to-day business

VAs (virtual assistants)

VAs are computer programmes which can complete basic tasks on your behalf, like creating reminders, sending emails or scheduling appointments. They can easily be integrated with other apps, such as those on your PC, laptop or phone, preventing the need to switch between different programmes when you want to get things done quickly – e.g. such as running errands online or sending an email as you get ready to call it a day at the office!


A chatbot uses AI to simulate conversations with people. When your customer inputs a query, the chatbot may have a set amount of responses to common questions or phrases, and choose the best one accordingly. The goal here is to minimise the time your team spends interacting with computers just to assist customers, and maximise the time they spend on helping you grow your business.

The same can be said for you customers as chatbots minimise the time they spend getting ‘support’ and maximise the time they spend buying and using your products or services.

Customer support

Customer support agents are leveraging NLU technology and tools to acquire information from customers on the phone without needing to type out every questions

Agents are now helping customers with complex issues through NLU technology and NLG tools, creating more personalised responses based on each customer’s unique situation – without having to type out entire sentences themselves.

Data capture

Data capture applications enable users to enter specific information on a web form using NLP matching instead of typing everything out manually on their keyboard. This makes it a lot quicker for users because there’s no longer a need to remember what each field is for or how to fill it up correctly with their keyboard.

When you order something online and you’ve ordered from a certain online store in the past, you may have noticed how when you enter your name again, details like your address, number and credit card information automatically get filled out in a single tap. That’s NLU-powered data capture at work!

Message routing and IVR

NLU systems are used on a daily basis for answering customer calls and routing them to the appropriate department. IVR systems allow you to handle customer queries and complaints on a 24/7 basis without having to hire extra staff or pay your current staff for any overtime hours.

RPA software

Robotic process automation (RPA) is an exciting software-based technology which utilises bots to automate routine tasks within applications which are meant for employee use only. Many professional solutions in this category utilise NLP and NLU capabilities to quickly understand massive amounts of text in documents and applications.

Market intelligence

The right market intelligence software can give you a massive competitive edge, helping you gather publicly available information quickly on other companies and individuals, all pulled from multiple sources. This can be used to automatically create records or combine with your existing CRM data. With NLU integration, this software can better understand and decipher the information it pulls from the sources.

Contract analytics

Sophisticated contract analysis software helps to provide insights which are extracted from contract data, so that the terms in all your contracts are more consistent. The technology fuelling this is indeed NLU or natural language understanding.

Conversational interfaces

Voice-activated devices like Google Home and Amazon Alexa allow users to speak in a natural way when communicating with these devices. Through the use of NLU, conversational interfaces can easily understand and respond to human language by way of segmenting words and sentences, using semantic knowledge, and recognising grammar in order to infer intent – so that the desired action or output can be taken.

All in all, perhaps the most common and immediately relatable example of AI in natural language understanding is the voice recognition technology many of us use on a daily basis – where voice recognition software analyses spoken words, converting them into text or the desired data for the computer to process. Alexa is exactly that, allowing users to input commands through voice instead of typing them in.

Facebook’s Messenger utilises AI, natural language understanding (NLU) and NLP to aid users in communicating more effectively with their contacts who may be living halfway across the world.

Some of the capabilities your NLU technology should have

Automated actions

A great NLU solution will create a well-developed interdependent network of data & responses, allowing specific insights to trigger actions automatically.

Integration options and ease of usability

Your NLU solution should also be simple and user-friendly enough to allow all your team members to use it easily, irrespective of their technological abilities or computer skills – e.g. integrating easily with the software that you like to use for project management and execution.

In-depth analysis

An ideal natural language understanding or NLU solution should be built to utilise an extensive bank of data and analysis to recognise the entities and relationships between them. It should be able to easily understand even the most complex sentiment and extract motive, intent, effort, emotion, and intensity easily, and as a result, make the correct inferences and suggestions.

It should also have the ability to train itself and learn continuously to improve over time.

The NLU solutions and systems at Fast Data Science use advanced AI and ML techniques to extract, tag, and rate concepts which are relevant to customer experience analysis, business intelligence and insights, and much more.

Our solutions can help you find topics and sentiment automatically in human language text, helping to bring key drivers of customer experiences to light within mere seconds. Easily detect emotion, intent, and effort with over a hundred industry-specific NLU models to better serve your audience’s underlying needs. Gain business intelligence and industry insights by quickly deciphering massive volumes of unstructured data.

Final thoughts

Hopefully, this article has explained “what is natural language understanding”, helping you see how it can benefit your business in multiple ways.

Times are changing and businesses are doing everything to improve cost-efficiencies and serve their customers on their own terms. In an uncertain global economy and business landscape, one of the best ways to stay competitive is to utilise the latest, greatest, and most powerful natural language understanding AI technologies currently available.

At Fast Data Science, we can develop customised NLU tools, technologies and systems according to your industry, business goals, and audience needs. [Call us]{.underline} now to learn more.

Unlock Your Future in NLP!

Dive into the world of Natural Language Processing! Explore cutting-edge NLP roles that match your skills and passions.

Explore NLP Jobs

Generative AI
Generative ai

Generative AI

Generative AI Introduction Generative AI, a subset of AI, is fundamentally transforming industries and shaping the future. Leveraging advanced algorithms, generative AI can create content, designs, and solutions that were previously unimaginable.

Big data
Big data

Big data

Big Data The emergence of big data has revolutionized industries, transforming traditional business models and decision-making processes. In this comprehensive exploration, we delve into what big data is, its significant impacts on business strategy, and how companies can leverage vast datasets to drive innovation and competitive advantage.

AI in finance
Ai in finance

AI in finance

AI in Finance The integration of artificial intelligence (AI) into the finance sector has revolutionized how institutions operate, from automating operations to enhancing customer engagement and improving risk management.

What we can do for you

Transform Unstructured Data into Actionable Insights

Contact us