previous icon Back to blog
Feb 14, 2022
7 minutes read

What are the differences between NLP, NLU and NLG?

Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) are technologies that are evolving fast. You may have heard of NLP, but what about its close relatives, NLU and NLG?

Camilla Holroyd
Camilla Holroyd,
Marketing Manager, UK & IE

Artificial Intelligence, or AI, is one of the most talked about technologies of the modern era. The potential for artificial intelligence to create labour-saving workarounds is near-endless and, as such, AI has become a buzzword for those looking to increase efficiencies in their work and to automate elements of their jobs.

Natural Language Processing (NLP), and it’s close relatives Natural Language Understanding (NLU) and Natural Language Generation (NLG), are subsets of AI that are specifically concerned with understanding human linguistic behaviour and the nuances of language that can lead machines to fully understand the needs of their human operators. 

Here’s a quick overview of the differences between NLP, NLU, and NLG. 

What is NLP or Natural Language Processing?

Natural Language Processing, or NLP, involves the processing of human language by a computer program to determine what its meaning is. 

Natural Language Processing is at the core of all conversational AI platforms. In conversational AI interactions, a machine must deduce meaning from a line of text by converting it into a data form it can understand. This allows it to select an appropriate response based on keywords it detects within the text. Other Natural Language Processing tasks include text translation, sentiment analysis and speech recognition. 

NLP generally uses one of two approaches: a rule-based approach or an AI-based approach.

Rule-based approach

Using a set of linguistic guidelines coded into the platform that use human grammatical structures. However, this approach requires the formulation of rules by a skilled linguist and must be kept-up-to-date as issues are uncovered. This can be a drain on resource in some circumstances, and the rule book can quickly become very complex, with rules that can sometimes contradict each other. 

AI-based approach

This is an algorithmic approach that uses statistical analysis of ‘training’ documents to establish rules and build its knowledge base. However, because language and grammar rules can be complex and contradictory, without human oversight and correction, this algorithmic approach can sometimes produce incorrect results. 

Given that the pros and cons of rule-based and AI-based approaches are largely complementary, CM.com’s unique method combines both approaches. This allows us to find the best way to engage with users on a case-by-case basis. 

What is NLU or Natural Language Understanding?

Natural Language Understanding, or NLU, is a subset of NLP. NLU is concerned with understanding the text so that it can be processed later. NLU is specifically scoped to understanding text by extracting meaning from it in a machine-readable way for future processing. NLP is about more than just understanding the text however. Because NLU encapsulates processing of the text alongside understanding it, NLU is a discipline within NLP.. NLU enables human-computer interaction in the sense that as well as being able to convert the human input into a form the computer can understand, the computer is now able to understand the intent of the query. Once the intent is understood, NLU allows the computer to formulate a coherent response to the human input. 

In the context of a conversational AI platform, if a user were to input the phrase ‘I want to buy an iPhone,’ the system would be able to understand that their intent is to make a purchase and that the entity they wish to purchase is an iPhone. This allows the system to provide a structured, relevant response based on the intents and entities provided in the query. That might involve sending the user directly to a product page, or initiating a set of production option pages before sending a direct link to purchase the item. 

NLU is particularly effective with homonyms – words that are spelled the same but that have different meanings, such as ‘bank’ – meaning a financial institution – and ‘bank’ – meaning a river bank for example. Human speech is complex, so the ability to interpret context from a string of words is hugely important. 

Natural Language Understanding is a vital part of the NLP process, which allows a conversational AI platform to extract intent from human input and formulate a response, whether that’s from a scripted range, or an AI-driven process.

What is NLG or Natural Language Generation?

Natural Language Generation, or NLG, takes the data it has collated from a human interaction and creates a response that can be understood by a human. Natural Language Generation is, by its nature, extremely complex and requires a multi-layer approach to process data into a response that a human will understand. Once the input has been processed, the data goes through a number of stages before the software formulates a response; including using sentence aggregation to accurately summarise the topic, and grammatical structuring to ensure the response can be understood effectively and sounds like it was created by a human rather than a machine.

NLG is a complex subject. Getting consistently high-quality responses to user queries is a challenge, but NLG has huge potential to revolutionise areas such as customer service, where huge amounts of time responding and structuring data to often repetitive queries. NLG is also being used to create templated content for a number of news outlets: data-driven report writing for example, where figures change but the structure remains fairly consistent. NLG is also a focus of much of our current research.

NLP, AI, And Machine Learning: Complimentary technologies

Language processing is a hugely significant technology in its own right, but it can also enhance a number of existing technologies, often without a full ‘rip and replace’ of legacy systems.

Interactive Voice Response (IVR)

Interactive Voice Response technology will be familiar to many of us. It allows callers to interact with an automated assistant without the need to speak to a human and resolve issues via a series of predetermined automated questions and responses. 

Natural Language Processing allows an IVR solution to understand callers, detect emotion and identify keywords in order to fully capture their intent and respond accordingly. Ultimately, the goal is to allow the Interactive Voice Response system to handle more queries, and deal with them more effectively with the minimum of human interaction to reduce handling times.  

voicebot human handoverWith NLP integrated into an IVR, it becomes a voicebot solution as opposed to a strict, scripted IVR solution. Voicebots allow direct, contextual interaction with the computer software via NLP technology, allowing the Voicebot to understand and respond with a relevant answer to a non-scripted question. 

Robotic Process Automation (RPA)

Robotic Process Automation, also known as RPA, is a method whereby technology takes on repetitive, rules-based data processing that may traditionally have been done by a human operator. Both Conversational AI and RPA automate previous manual processes but in a markedly different way. Increasingly, however, RPA is being referred to as IPA, or Intelligent Process Automation, using AI technology to understand and take on increasingly complex tasks. 

How does conversational AI work?

Conversational AI employs natural language understanding, machine learning, and natural language processing to engage in customer conversations. Natural language understanding helps decipher the meaning of users’ words (even with their quirks and mistakes!) and remembers what has been said to maintain context and continuity.

Once a customer’s intent is understood, machine learning determines an appropriate response. This response is converted into understandable human language using natural language generation.

DGP chatbot human agent questions

NLP processes flow through a continuous feedback loop with machine learning to improve the computer’s artificial intelligence algorithms. Rather than relying on keyword-sensitive scripts, NLU creates unique responses based on previous interactions. 

Get started with conversational AI

If you want to know more about Natural Language Processing, Understanding, and Generation, and its potential to create efficiencies in your business, get in touch and we can discuss how our technology can help you to fast track your digital transformation.

Learn more about improving your customer experience with Conversational AI

Was this article interesting?
Share it!
Camilla Holroyd
Camilla Holroyd,
Marketing Manager, UK & IE
logo linkedin icon

As Marketing Manager for the UK & IE, Camilla manages the region's marketing strategy, focusing on CM.com's Engagement, Connectivity and Ticketing platforms. She is passionate about promoting how technology improves CX and enjoys speaking to CM.com's customers about their insights and experiences.

Related articles

hero-17-sms-customer-service-templates-to-use-today
Nov 11, 2024 • Conversational AI

Turning shoppers into loyal fans: how to retain customers post-Black Friday

Black Friday is a huge shopping event, drawing crowds of eager shoppers hunting for deals. But after the frenzy fades, the real challenge begins: turning one-time shoppers into loyal, repeat customers. Customer retention is vital for long-term success and post-Black Friday is the perfect time to build lasting relationships. So, how can your business retain customers post-Black Friday? In this blog, we’ll explore how to make it happen.

blackfriday-2024 blog
Oct 07, 2024 • Conversational AI

The art of simplicity: help customers make quick Black Friday decisions

Black Friday 2024 is just around the corner, bringing with it a great opportunity for retailers to maximise sales. However, standing out in such a competitive event isn’t easy. Consumers expect attractive offers, fast deliveries and top-notch customer support. In this article, we’ll show you how to simplify the purchasing process during Black Friday, optimising everything from promotions to logistics and 24/7 customer service to ensure a seamless experience and increase customer loyalty.

engage-platform-effect-customer-service
May 13, 2024 • CM.com

Happy clients, happy agents: the platform effect in customer service

As a member of the customer service team, you stand on the frontline of customer interaction every day. In a world where customers demand quick and personalised service, long wait times, impersonal responses, or worse, incorrect answers, can quickly drive a customer away. Your goal, however, is to connect customers with your organisation and deliver the best answers and service possible.

Education Technology
Nov 27, 2023 • Conversational AI

How Generative AI supercharges your customer service

Meeting customers' expectations remains the biggest challenge in service. Speed, convenience, and accurate responses are critical to achieving this. With the power of Generative AI, customer questions can be identified, categorised, and resolved more quickly. Plus, your organisation is continuously fed with data to improve the entire customer journey.

Acquisition and retention in marketing engage platform
Nov 13, 2023 • Conversational AI

Decoding the struggles of acquisition and retention

In life, they say percentages don't matter, but in marketing, they are everything. The customer journey, spanning from acquisition to retention, is a path filled with potential incremental drop-offs at every touchpoint. A confusing experience here, an ill-timed communication there, and suddenly, your conversion rate is plummeting.

chatbot-customer-experience
Sep 18, 2023 • Chatbots

Omnichannel chatbots: create once, offer everywhere

Chatbots spent a decade-plus as a technological sideline: nestling at the corner of websites, roaming the odd FAQ, inviting people to click with a hopeful link. They weren’t a big part of the customer experience. But now – suddenly – they’re everywhere.

blog-image-chatbots-improve-customer-service
Aug 07, 2023 • Chatbots

How to use AI chatbots to improve customer service

Customer service teams face a vast range of challenges in their day-to-day. Customer expectations are higher than ever, and large volumes of repetitive queries can test their patience and create stress where there needn’t be any. That’s likely why the average customer service representative only stays in a job for 12 months.

dutch-grand-prix-service
Jun 30, 2023 • CM.com

Seamless customer support at the Formula 1 Heineken Dutch Grand Prix

In the run-up to any event, attendees will have numerous questions, such as "how do I get there," "where can I find my tickets," and "Is there somewhere to stay nearby." F1 fans often ask these types of questions before the Formula 1 Heineken Dutch Grand Prix.

Generative AI chatGPT blog about new updates
Jun 22, 2023 • Conversational AI

CM.com's next steps into Generative AI: upcoming releases for 2023

The market for generative AI has experienced significant growth, with over $14.8 billion of venture capital invested in startups building their products on Large Language Models like OpenAI’s ChatGPT and other generative AI tools. The space is booming, evident from the high number of website domain registrations in the field every week. The key challenge for most companies is to find out what will propel their businesses moving forward.

Is this region a better fit for you?
Go
close icon