NLP vs NLU: Whats The Difference? BMC Software Blogs

NLP vs NLU vs NLG: Understanding the Differences by Tathagata Medium

what does nlu mean

Pushing the boundaries of possibility, natural language understanding (NLU) is a revolutionary field of machine learning that is transforming the way we communicate and interact with computers. Being able to formulate meaningful answers in response to users’ questions is the domain of expert.ai Answers. This expert.ai solution supports businesses through customer experience management and automated personal customer assistants.

Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently. Help your business get on the right track to analyze and infuse your data at scale for AI. Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time. Natural language understanding (NLU) is a part of artificial intelligence (AI) focused on teaching computers how to understand and interpret human language as we use it naturally. In machine learning (ML) jargon, the series of steps taken are called data pre-processing.

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NLP takes input text in the form of natural language, converts it into a computer language, processes it, and returns the information as a response in a natural language. NLU converts input text or speech into structured data and helps extract facts from this input data. These are all good reasons for giving natural language understanding a go, but how do you know if the accuracy of an algorithm will be sufficient?. You can foun additiona information about ai customer service and artificial intelligence and NLP. Consider the type of analysis it will need to perform and the breadth of the field. Analysis ranges from shallow, such as word-based statistics that ignore word order, to deep, which implies the use of ontologies and parsing.

For example, a virtual assistant might use NLU to understand a user’s request to book a flight and then generate a response that includes flight options and pricing information. NLU is the final step in NLP that involves a machine learning process to create an automated system capable of interpreting human input. This requires creating a model that has been trained on labelled training data, including what is being said, who said it and when they said it (the context). With advances in AI technology we have recently seen the arrival of large language models (LLMs) like GPT. LLM models can recognize, summarize, translate, predict and generate languages using very large text based dataset, with little or no training supervision.

Logic is applied in the form of an IF-THEN structure embedded into the system by humans, who create the rules. This hard coding of rules can be used to manipulate the understanding of symbols. Machine learning uses computational methods to train models on data and adjust (and ideally, improve) its methods as more data is processed. Also, NLU can generate targeted content for customers based on their preferences and interests. Over 60% say they would purchase more from companies they felt cared about them.

Key Components of NLP, NLU, and NLG

The idea is to break down the natural language text into smaller and more manageable chunks. These can then be analyzed by ML algorithms to find relations, dependencies, and context among various chunks. The core capability of NLU technology is to understand language in the same way humans do instead of relying on keywords to grasp concepts. As language recognition software, NLU algorithms can enhance the interaction between humans and organizations while also improving data gathering and analysis. Natural language understanding implements algorithms that analyze human speech and break it down into semantic and pragmatic definitions. NLU technology aims to capture the intent behind communication and identify entities, such as people or numeric values, mentioned during speech.

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By understanding the differences between these three areas, we can better understand how they are used in real-world applications and how they can be used to improve our interactions with computers and AI systems. One of the most common applications of NLP is in chatbots and virtual assistants. These systems use NLP to understand the user’s input and generate a response that is as close to human-like as possible. NLP is also used in sentiment analysis, which is the process of analyzing text to determine the writer’s attitude or emotional state. NLU is technically a sub-area of the broader area of natural language processing (NLP), which is a sub-area of artificial intelligence (AI).

NLP Vs. NLU Vs. NLG: What’s The Difference?

Statistical models use machine learning algorithms such as deep learning to learn the structure of natural language from data. Hybrid models combine the two approaches, using machine learning algorithms to generate rules and then applying those rules to the input data. Natural language understanding gives us the ability to bridge the communicational gap between humans and computers. NLU empowers artificial intelligence to offer people assistance and has a wide range of applications. For example, customer support operations can be substantially improved by intelligent chatbots.

  • NLU is used in a variety of applications, including virtual assistants, chatbots, and voice assistants.
  • NLP is about understanding and processing human language.NLU is about understanding human language.NLG is about generating human language.
  • While NLU, NLP, and NLG are often used interchangeably, they are distinct technologies that serve different purposes in natural language communication.
  • Contact center operators and CX leaders want to improve customer experience, increase revenue generation and reduce compliance risk.

For example, the term “bank” can have different meanings depending on the context in which it is used. If someone says they are going to the “bank,” they could be going to a financial institution or to the edge of a river. Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation. Simplilearn’s AI ML Certification is designed after our intensive Bootcamp learning model, so you’ll be ready to apply these skills as soon as you finish the course. You’ll learn how to create state-of-the-art algorithms that can predict future data trends, improve business decisions, or even help save lives. SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items.

It has been shown to increase productivity by 20% in contact centers and reduce call duration by 50%. Beyond contact centers, NLU is being used in sales and marketing automation, virtual assistants, and more. On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU.

Millions of organisations are already using AI-based natural language understanding to analyse human input and gain more actionable insights. On the contrary, natural language understanding (NLU) is becoming highly critical in business across nearly every sector. To further grasp “what is natural language understanding”, what does nlu mean we must briefly understand both NLP (natural language processing) and NLG (natural language generation). Natural Language Understanding is a big component of IVR since interactive voice response is taking in someone’s words and processing it to understand the intent and sentiment behind the caller’s needs.

Advancements in NLP, NLU, and NLG

On average, an agent spends only a quarter of their time during a call interacting with the customer. That leaves three-quarters of the conversation for research–which is often manual and tedious. But when you use an integrated system that ‘listens,’ it can share what it learns automatically- making your job much easier. In other words, when a customer asks a question, it will be the automated system that provides the answer, and all the agent has to do is choose which one is best. With an agent AI assistant, customer interactions are improved because agents have quick access to a docket of all past tickets and notes.

what does nlu mean

It uses algorithms and artificial intelligence, backed by large libraries of information, to understand our language. In this case, the person’s objective is to purchase tickets, and the ferry is the most likely form of travel as the campground is on an island. A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand.

Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason. In today’s hyperconnected world, our smartphones have become inseparable companions, constantly gathering and transmitting data about our whereabouts and movements. This trove of information, often referred to as mobile traffic data, holds a wealth of insights about human behaviour within cities, offering a unique perspective on urban dynamics and patterns of movement. Imagine how much cost reduction can be had in the form of shorter calls and improved customer feedback as well as satisfaction levels.

what does nlu mean

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. 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. NLP is a process where human-readable text is converted into computer-readable data.

For example, it is difficult for call center employees to remain consistently positive with customers at all hours of the day or night. However, a chatbot can maintain positivity and safeguard your brand’s reputation. Chatbots offer 24-7 support and are excellent problem-solvers, often providing instant solutions to customer inquiries.

While progress is being made, a machine’s understanding in these areas is still less refined than a human’s. This book is for managers, programmers, directors – and anyone else who wants to learn machine learning. The first successful attempt came out in 1966 in the form of the famous ELIZA program which was capable of carrying on a limited form of conversation with a user. NLP can process text from grammar, structure, typo, and point of view—but it will be NLU that will help the machine infer the intent behind the language text. So, even though there are many overlaps between NLP and NLU, this differentiation sets them distinctly apart. A task called word sense disambiguation, which sits under the NLU umbrella, makes sure that the machine is able to understand the two different senses that the word “bank” is used.

It is also beneficial in understanding brand perception, helping you figure out how your customers (and the market in general) feel about your brand and your offerings. Now that you know how does Natural language understanding (NLU) work, and how it is used in various areas. A lot of acronyms get tossed around when discussing artificial intelligence, and NLU is no exception. NLU, a subset of AI, is an umbrella term that covers NLP and natural language generation (NLG). Whether you’re dealing with an Intercom bot, a web search interface, or a lead-generation form, NLU can be used to understand customer intent and provide personalized responses. NLU provides many benefits for businesses, including improved customer experience, better marketing, improved product development, and time savings.

Real-time agent assist applications dramatically improve the agent’s performance by keeping them on script to deliver a consistent experience. Similarly, supervisor assist applications help supervisors to give their agents live assistance when they need the most, thereby impacting the outcome positively. With AI-driven thematic analysis software, you can generate actionable insights effortlessly. In essence, NLU, once a distant dream of the AI community, now influences myriad aspects of our digital interactions. From the movies we watch to the customer support we receive — it’s an invisible hand, guiding and enhancing our experiences.

what does nlu mean

Customer support agents can leverage NLU technology to gather information from customers while they’re on the phone without having to type out each question individually. The voice assistant uses the framework of Natural Language Processing to understand what is being said, and it uses Natural Language Generation to respond in a human-like manner. There is Natural Language Understanding at work as well, helping the voice assistant to judge the intention of the question. For example, using NLG, a computer can automatically generate a news article based on a set of data gathered about a specific event or produce a sales letter about a particular product based on a series of product attributes.

Additionally, NLU establishes a data structure specifying relationships between phrases and words. While humans can do this naturally in conversation, machines need these analyses to understand what humans mean in different texts. While NLP analyzes and comprehends the text in a document, NLU makes it possible to communicate with a computer using natural language.

In this context, another term which is often used as a synonym is Natural Language Understanding (NLU). While NLU processes may seem instantaneous to the casual observer, there is much going on behind the scenes. Data must be gathered, organized, analyzed, and delivered before it is made functional. For example, when a human reads a user’s question on Twitter and replies with an answer, or on a large scale, like when Google parses millions of documents to figure out what they’re about. Your NLU solution should be simple to use for all your staff no matter their technological ability, and should be able to integrate with other software you might be using for project management and execution.

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. 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. NLP and NLU are similar but differ in the complexity of the tasks they can perform. NLP focuses on processing and analyzing text data, such as language translation or speech recognition.

How does LASER perform NLP tasks?

Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. This is particularly important, given the scale of unstructured text that is generated on an everyday basis. NLU-enabled technology will be needed to get the most out of this information, and save you time, money and energy to respond in a way that consumers will appreciate. Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language.

what does nlu mean

This is especially important for model longevity and reusability so that you can adapt your model as data is added or other conditions change. Manual ticketing is a tedious, inefficient process that often leads to delays, frustration, and miscommunication. This technology allows your system to understand the text within each ticket, effectively filtering and routing tasks to the appropriate expert or department.

what does nlu mean

These low-friction channels allow customers to quickly interact with your organization with little hassle. By 2025, the NLP market is expected to surpass $43 billion–a 14-fold increase from 2017. Businesses worldwide are already relying on NLU technology to make sense of human input and gather insights toward improved decision-making. For example, a computer can use NLG to automatically generate news articles based on data about an event. It could also produce sales letters about specific products based on their attributes. AI technology has become fundamental in business, whether you realize it or not.

Akkio is used to build NLU models for computational linguistics tasks like machine translation, question answering, and social media analysis. With Akkio, you can develop NLU models and deploy them into production for real-time predictions. NLG can be used to generate natural language summaries of data or to generate natural language instructions for a task such as how to set up a printer. NLU is the process of understanding a natural language and extracting meaning from it. NLU can be used to extract entities, relationships, and intent from a natural language input.

what does nlu mean

In this exploration, we’ll delve deeper into the nuances of NLU, tracing its evolution, understanding its core components, and recognizing its potential and pitfalls. Chatbots are likely the best known and most widely used application of NLU and NLP technology, one that has paid off handsomely for many companies that deploy it. For example, clothing retailer Asos was able to increase orders by 300% using Facebook Messenger Chatbox, and it garnered a 250% ROI increase while reaching almost 4 times more user targets.

This can free up your team to focus on more pressing matters and improve your team’s efficiency. If customers are the beating heart of a business, product development is the brain. NLU can be used to gain insights from customer conversations to inform product development decisions. Bharat Saxena has over 15 years of experience in software product development, and has worked in various stages, from coding to managing a product. His current active areas of research are conversational AI and algorithmic bias in AI. Gone are the days when chatbots could only produce programmed and rule-based interactions with their users.

It uses a combinatorial process of analytic output and contextualized outputs to complete these tasks. The rise of chatbots can be attributed to advancements in AI, particularly in the fields of natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG). These technologies allow chatbots to understand and respond to human language in an accurate and natural way. Natural Language Generation (NLG) is another subset of natural language processing. NLG enables AI systems to produce human language text responses based on some data input. Using NLG, contact centers can quickly generate a summary from the customer call.

Natural language understanding can help speed up the document review process while ensuring accuracy. With NLU, you can extract essential information from any document quickly and easily, giving you the data you need to make fast business decisions. With today’s mountains of unstructured data generated daily, it is essential to utilize NLU-enabled technology. The technology can help you effectively communicate with consumers and save the energy, time, and money that would be expensed otherwise. Typical computer-generated content will lack the aspects of human-generated content that make it engaging and exciting, like emotion, fluidity, and personality. However, NLG technology makes it possible for computers to produce humanlike text that emulates human writers.

The NLU-based text analysis can link specific speech patterns to negative emotions and high effort levels. Using predictive modeling algorithms, you can identify these speech patterns automatically in forthcoming calls and recommend a response from your customer service representatives as they are on the call to the customer. This reduces the cost to serve with shorter calls, and improves customer feedback.

NLP is a type of artificial intelligence that focuses on empowering machines to interact using natural, human languages. It also enables machines to process huge amounts of natural language data and derive insights from that data. It rearranges unstructured data so that the machine can understand and analyze it. In its essence, NLU helps machines interpret natural language, derive meaning and identify context from it. Alexa is exactly that, allowing users to input commands through voice instead of typing them in.

Similarly, cosmetic giant Sephora increased its makeover appointments by 11% by using Facebook Messenger Chatbox. If you are using a live chat system, you need to be able to route customers to an agent that’s equipped to answer their questions. You can’t afford to force your customers to hop across dozens of agents before they finally reach the one that can answer their question. The largest community of AI agent builders and conversational AI teams focused on sharing projects, benchmarks, best practices and creating the best assistants across every industry. Social media analysis with NLU reveals trends and customer attitudes toward brands and products.