NLP vs NLU vs. NLG: the differences between three natural language processing concepts

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. In this context, another term which is often used as a synonym is Natural Language Understanding (NLU). Classifies content into pre-existing categories by function, intention or purpose. 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. We also offer an extensive library of use cases, with templates showing different AI workflows. Akkio also offers integrations with a wide range of dataset formats and sources, such as Salesforce, Hubspot, and Big Query.

Whether they’re directing users to a product, answering a support question, or assigning users to a human customer-support operator, NLU chatbots offer an effective, efficient, and affordable way to support customers in real time. NLU is used in a variety of applications, including virtual assistants, chatbots, and voice assistants. These systems use NLU to understand the user’s input and generate a response that is tailored to their needs. 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. 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.

Traditional search engines work well for keyword-based searches, but for more complex queries, an NLU search engine can make the process considerably more targeted and rewarding. Suppose that a shopper queries “Show me classy black dresses for under $500.”  This query defines the product (dress), product type (black), price point (less than $500), and personal tastes and preferences (classy). Ecommerce websites rely heavily on sentiment analysis of the reviews and feedback from the users—was a review positive, negative, or neutral? Here, they need to know what was said and they also need to understand what was meant. Based on your application, our team will build a customized NLU model utilizing pre-built classifiers and entity dictionaries as the base, and then incorporate customized language solutions based on your application needs.

Related Software

Natural language processing is the process of turning human-readable text into computer-readable data. It’s used in everything from online search engines to chatbots that can understand our questions and give us answers based on what we’ve typed. With text analysis solutions like MonkeyLearn, machines can understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket. Not only does this save customer support teams hundreds of hours, but it also helps them prioritize urgent tickets. Trying to meet customers on an individual level is difficult when the scale is so vast.

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. Natural language understanding (NLU) refers to a computer’s ability to understand or interpret human language.

Using data modelling to learn what we really mean

Learn how to extract and classify text from unstructured data with MonkeyLearn’s no-code, low-code text analysis tools. With natural language processing and machine learning working behind the scenes, all you need to focus on is using the tools and helping them to improve their natural language understanding. However, true understanding of natural language is challenging due to the complexity and nuance of human communication. Machine learning approaches, such as deep learning and statistical models, can help overcome these obstacles by analyzing large datasets and finding patterns that aid in interpretation and understanding. Overall, text analysis and sentiment analysis are critical tools utilized in NLU to accurately interpret and understand human language.

Essentially, before a computer can process language data, it must understand the data. 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. This enables machines to produce more accurate and appropriate responses during interactions. As humans, we can identify such underlying similarities almost effortlessly and respond accordingly.

Taking action and forming a response

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). NLU can help you save time by automating customer service tasks like answering FAQs, routing customer requests, and identifying customer problems. This can free up your team to focus on more pressing matters and improve your team’s efficiency.

NLU-powered chatbots and virtual assistants can accurately recognize user intent and respond accordingly, providing a more seamless customer experience. Natural Language Understanding (NLU) refers to the ability of a machine to interpret and generate human language. However, NLU systems face numerous challenges while processing natural language inputs. Natural Language Understanding (NLU) plays a crucial role in the development and application of Artificial Intelligence (AI).

For example, entity analysis can identify specific entities mentioned by customers, such as product names or locations, to gain insights into what aspects of the company are most discussed. Sentiment analysis can help determine the overall attitude of customers towards the company, while content analysis can reveal common themes and topics mentioned in customer feedback. It involves understanding the intent behind a user’s input, whether it be a query or a request.

This not only saves time and effort but also improves the overall customer experience. One of the major applications of NLU in AI is in the analysis of unstructured text. With the increasing amount of data available in the digital world, NLU inference services can help businesses gain valuable insights from text data sources such as customer feedback, social media posts, and customer service tickets. The purpose of NLU is to understand human conversation so that talking to a machine becomes just as easy as talking to another person.

NLU thereby allows computer software and applications to be more accurate and useful in responding to written and spoken commands. It’s important for developers to consider the difference between NLP and NLU when designing conversational search functionality because it impacts the quality of interpretation of what users say and mean. Essentially, NLP processes what was said or entered, while NLU endeavors to understand what was meant. The intent of what people write or say can be distorted through misspelling, fractured sentences, and mispronunciation. NLU pushes through such errors to determine the user’s intent, even if their written or spoken language is flawed. NLP involves processing natural spoken or textual language data by breaking it down into smaller elements that can be analyzed.

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. 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.

Additionally, the NLG system must decide on the output text’s style, tone, and level of detail. The difference between natural language understanding and natural language generation is that the former deals with a computer’s ability to read comprehension, while the latter pertains to a machine’s writing capability. Natural Language Understanding (NLU) connects with human communication’s deeper meanings and purposes, such as feelings, objectives, or motivation. It employs AI technology and algorithms, supported by massive data stores, to interpret human language. Grammar complexity and verb irregularity are just a few of the challenges that learners encounter. Now, consider that this task is even more difficult for machines, which cannot understand human language in its natural form.

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. What’s more, you’ll be better positioned to respond to the ever-changing needs of your audience. At times, NLU is used in conjunction with NLP, ML (machine learning) and NLG to produce some very powerful, customised solutions for businesses. A Large Language Model (LLM) is an advanced artificial intelligence system that processes and generates human language. In general, NLP is focused on the technical aspects of processing and manipulating language, while NLU is concerned with understanding the meaning and context of language.

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. As machine learning techniques were developed, the ability to parse language and extract meaning from it has moved from deterministic, rule-based approaches to more data-driven, statistical approaches.

According to various industry estimates only about 20% of data collected is structured data. The remaining 80% is unstructured data—the majority of which is unstructured text data that’s unusable for traditional methods. Just think of all the online text you nlu meaning consume daily, social media, news, research, product websites, and more. Going back to our weather enquiry example, it is NLU which enables the machine to understand that those three different questions have the same underlying weather forecast query.

• NLG is another subcategory of NLP that constructs sentences based on a given semantic.
• Addressing lexical, syntax, and referential ambiguities, and understanding the unique features of different languages, are necessary for efficient NLU systems.
• AppTek’s NLU technology empowers organizations across a wide field of business applications who want to dive further into the meaning of spoken, written or translated sentences across a broad range of languages.
• Natural language understanding (NLU) is a technical concept within the larger topic of natural language processing.

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. This data-driven approach provides the information they need quickly, so they can quickly resolve issues – instead of searching multiple channels for answers.