What is the Difference Between NLP, NLU, and NLG?

Natural language understanding is a branch of AI that understands sentences using text or speech. NLU allows machines to understand human interaction by using algorithms to reduce human speech into structured definitions and concepts for understanding relationships. NLU can understand and process the meaning of speech or text of a natural language. To do so, NLU systems need a lexicon of the language, a software component called a parser for taking input data and building a data structure, grammar rules, and semantics theory. Natural Language Understanding(NLU) is an area of artificial intelligence to process input data provided by the user in natural language say text data or speech data. It is a way that enables interaction between a computer and a human in a way like humans do using natural languages like English, French, Hindi etc.

Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers. NLP and NLU have unique strengths and applications as Chat PG mentioned above, but their true power lies in their combined use. Integrating both technologies allows AI systems to process and understand natural language more accurately. Together, NLU and natural language generation enable NLP to function effectively, providing a comprehensive language processing solution.

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6 min read – Get the key steps for creating an effective customer retention strategy that will help retain customers and keep your business competitive. Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. 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. Before booking a hotel, customers want to learn more about the potential accommodations. People start asking questions about the pool, dinner service, towels, and other things as a result.

Even more, in the real life, meaningful sentences often contain minor errors and can be classified as ungrammatical. Human interaction allows for errors in the produced text and speech compensating them by excellent pattern recognition and drawing additional information from the context. This shows the lopsidedness of the syntax-focused analysis and the need for a closer focus on multilevel semantics. As can be seen by its tasks, NLU is the integral part of natural language processing, the part that is responsible for human-like understanding of the meaning rendered by a certain text.

However, navigating the complexities of natural language processing and natural language understanding can be a challenging task. This is where Simform’s expertise in AI and machine learning development services can help you overcome those challenges and leverage cutting-edge language processing technologies. Natural language processing is generally more suitable for tasks involving data extraction, text summarization, and machine translation, among others. Meanwhile, NLU excels in areas like sentiment analysis, sarcasm detection, and intent classification, allowing for a deeper understanding of user input and emotions. On the other hand, natural language understanding is concerned with semantics – the study of meaning in language.

An effective NLP system takes in language and maps it — applying a rigid, uniform system to reduce its complexity to something a computer can interpret. Matching word patterns, understanding synonyms, tracking grammar — these techniques all help reduce linguistic complexity to something a computer can process. While both understand human language, NLU communicates with untrained individuals to learn and understand their intent. In addition to understanding words and interpreting meaning, NLU is programmed to understand meaning, despite common human errors, such as mispronunciations or transposed letters and words.

The Key Difference Between NLP and NLU

The noun it describes, version, denotes multiple iterations of a report, enabling us to determine that we are referring to the most up-to-date status of a file. Agentic AI represents an evolution in AI, with goal-directed behaviors and autonomous decision-making. Learn how AI knowledge bases enhance knowledge management by enabling continuous learning, personalized support, and knowledge discovery. Contact Moveworks to learn how AI can supercharge your workforce productivity. In this post we’ll scrutinize over the concepts of NLP and NLU and their niches in the AI-related technology.

The product they have in mind aims to be effortless, unsupervised, and able to interact directly with people in an appropriate and successful manner. With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5). As a result, they do not require both excellent NLU skills and intent recognition. Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions.

The terms might look like alphabet spaghetti but each is a separate concept. In fact, NLP includes NLU and NLG concepts to achieve human-like processing. All these sentences have the same underlying question, which is to enquire about today’s weather forecast.

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. After all, different sentences can mean the same thing, and, vice versa, the same words can mean different things depending on how they are used. From the computer’s point of view, any natural language is a free form text. That means there are no set keywords at set positions when providing an input. 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.

• He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade.
• ML algorithms can then examine these to discover relationships, connections, and context between these smaller sections.
• Applications for NLP are diversifying with hopes to implement large language models (LLMs) beyond pure NLP tasks (see 2022 State of AI Report).
• Meanwhile, NLU excels in areas like sentiment analysis, sarcasm detection, and intent classification, allowing for a deeper understanding of user input and emotions.
• The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker.

Questionnaires about people’s habits and health problems are insightful while making diagnoses. Behind the scenes, sophisticated algorithms like hidden Markov chains, recurrent neural networks, n-grams, decision trees, naive bayes, etc. work in harmony to make it all possible. The OneAI NLU Studio allows developers to combine NLU and NLP features with their applications in reliable and efficient ways.

And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly. Natural language understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input in the form of sentences using text or speech. NLU enables human-computer interaction by analyzing language versus just words. The fascinating world of human communication is built on the intricate relationship between syntax and semantics. While syntax focuses on the rules governing language structure, semantics delves into the meaning behind words and sentences. In the realm of artificial intelligence, NLU and NLP bring these concepts to life.

Correlation Between NLP and NLU

Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services. Ultimately, we can say that natural language understanding works by employing algorithms and machine learning models to analyze, interpret, and understand human language through entity and intent recognition.

If the evaluator is not able to reliably tell the difference between the response generated by the machine and the other human, then the machine passes the test and is considered to be exhibiting “intelligent” behavior. 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. A natural language is one that has evolved over time via use and repetition. Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time.

By working diligently to understand the structure and strategy of language, we’ve gained valuable insight into the nature of our communication. Building a computer that perfectly understands us is a massive challenge, but it’s far from impossible — it’s already happening with NLP and NLU. Hiren is VP of Technology at Simform with an extensive experience https://chat.openai.com/ in helping enterprises and startups streamline their business performance through data-driven innovation. NLU, however, understands the idiom and interprets the user’s intent as being hungry and searching for a nearby restaurant. Imagine planning a vacation to Paris and asking your voice assistant, “What’s the weather like in Paris?

People can express the same idea in different ways, but sometimes they make mistakes when speaking or writing. They could use the wrong words, write sentences that don’t make sense, or misspell or mispronounce words. NLP can study language and speech to do many things, but it can’t always understand what someone intends to say. NLU enables computers to understand what someone meant, even if they didn’t say it perfectly. NLU analyzes data using algorithms to determine its meaning and reduce human speech into a structured ontology consisting of semantic and pragmatic definitions.

Check out the OneAI Language Studio for yourself and see how easy the implementation of NLU capabilities can be. The OneAI Language Studio also generates the code for the selected skill or skills. These capabilities, and more, allow developers to experiment with NLU and build pipelines for their specific use cases to customize their text, audio, and video data further. Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) all fall under the umbrella of artificial intelligence (AI).

While NLP and NLU are not interchangeable terms, they both work toward the end goal of understanding language. There might always be a debate on what exactly constitutes NLP versus NLU, with specialists arguing about where they overlap or nlu and nlp diverge from one another. But, in the end, NLP and NLU are needed to break down complexity and extract valuable information. With NLP, we reduce the infinity of language to something that has a clearly defined structure and set rules.

NLU

With a greater level of intelligence, NLP helps computers pick apart individual components of language and use them as variables to extract only relevant features from user utterances. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, an increasingly data mining. The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text.

NLU’s core functions are understanding unstructured data and converting text into a structured data set which a machine can more easily consume. Applications vary from relatively simple tasks like short commands for robots to MT, question-answering, news-gathering, and voice activation. 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.

These methods have been shown to achieve state-of-the-art results for many natural language tasks. As humans, we can identify such underlying similarities almost effortlessly and respond accordingly. But this is a problem for machines—any algorithm will need the input to be in a set format, and these three sentences vary in their structure and format.

Everything you need to know about NLUs whether you’re a Developer, Researcher, or Business Owner.

A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand. You can foun additiona information about ai customer service and artificial intelligence and NLP. Instead of relying on computer language syntax, NLU enables a computer to comprehend and respond to human-written text. Natural language processing primarily focuses on syntax, which deals with the structure and organization of language. NLP techniques such as tokenization, stemming, and parsing are employed to break down sentences into their constituent parts, like words and phrases. This process enables the extraction of valuable information from the text and allows for a more in-depth analysis of linguistic patterns. For example, NLP can identify noun phrases, verb phrases, and other grammatical structures in sentences.

The “breadth” of a system is measured by the sizes of its vocabulary and grammar. The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding,[25] but they still have limited application. Systems that are both very broad and very deep are beyond the current state of the art. A number of advanced NLU techniques use the structured information provided by NLP to understand a given user’s intent.

While creating a chatbot like the example in Figure 1 might be a fun experiment, its inability to handle even minor typos or vocabulary choices is likely to frustrate users who urgently need access to Zoom. While human beings effortlessly handle verbose sentences, mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are typically less adept at handling unpredictable inputs. But while playing chess isn’t inherently easier than processing language, chess does have extremely well-defined rules.

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. Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant.

NLP is a branch of AI that allows more natural human-to-computer communication by linking human and machine language. Bharat Saxena has over 15 years of experience in software product development, and has worked in various stages, from coding to managing a product. With BMC, he supports the AMI Ops Monitoring for Db2 product development team.

His current active areas of research are conversational AI and algorithmic bias in AI. Since then, with the help of progress made in the field of AI and specifically in NLP and NLU, we have come very far in this quest. 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 the world of AI, for a machine to be considered intelligent, it must pass the Turing Test. A test developed by Alan Turing in the 1950s, which pits humans against the machine. The verb that precedes it, swimming, provides additional context to the reader, allowing us to conclude that we are referring to the flow of water in the ocean.

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. NLU makes it possible to carry out a dialogue with a computer using a human-based language. This is useful for consumer products or device features, such as voice assistants and speech to text.

NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response. To learn why computers have struggled to understand language, it’s helpful to first figure out why they’re so competent at playing chess. There are more possible moves in a game than there are atoms in the universe. By combining their strengths, businesses can create more human-like interactions and deliver personalized experiences that cater to their customers’ diverse needs. This integration of language technologies is driving innovation and improving user experiences across various industries. Whether it’s simple chatbots or sophisticated AI assistants, NLP is an integral part of the conversational app building process.

This enables machines to produce more accurate and appropriate responses during interactions. Sometimes people know what they are looking for but do not know the exact name of the good. In such cases, salespeople in the physical stores used to solve our problem and recommended us a suitable product. In the age of conversational commerce, such a task is done by sales chatbots that understand user intent and help customers to discover a suitable product for them via natural language (see Figure 6).

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. 7 min read – Six ways organizations use a private cloud to support ongoing digital transformation and create business value.

Artificial Intelligence (AI) is the creation of intelligent software or hardware to replicate human behaviors in learning and problem-solving areas. Worldwide revenue from the AI market is forecasted to reach USD 126 billion by 2025, with AI expected to contribute over 10 percent to the GDP in North America and Asia regions by 2030. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models. You can learn more about custom NLU components in the developer documentation, and be sure to check out this detailed tutorial. Slator explored whether AI writing tools are a threat to LSPs and translators. It’s possible AI-written copy will simply be machine-translated and post-edited or that the translation stage will be eliminated completely thanks to their multilingual capabilities.

NLP attempts to analyze and understand the text of a given document, and NLU makes it possible to carry out a dialogue with a computer using natural language. Thus, it helps businesses to understand customer needs and offer them personalized products. However, the full potential of NLP cannot be realized without the support of NLU. And so, understanding NLU is the second step toward enhancing the accuracy and efficiency of your speech recognition and language translation systems. Natural Language Understanding Applications are becoming increasingly important in the business world.

If NLP is about understanding the state of the game, NLU is about strategically applying that information to win the game. Thinking dozens of moves ahead is only possible after determining the ground rules and the context. Working together, these two techniques are what makes a conversational AI system a reality. Consider the requests in Figure 3 — NLP’s previous work breaking down utterances into parts, separating the noise, and correcting the typos enable NLU to exactly determine what the users need.

One of the biggest differences from NLP is that NLU goes beyond understanding words as it tries to interpret meaning dealing with common human errors like mispronunciations or transposed letters or words. Natural Language Processing, a fascinating subfield of computer science and artificial intelligence, enables computers to understand and interpret human language as effortlessly as you decipher the words in this sentence. From deciphering speech to reading text, our brains work tirelessly to understand and make sense of the world around us. However, our ability to process information is limited to what we already know.

Recent groundbreaking tools such as ChatGPT use NLP to store information and provide detailed answers. 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. Hence the breadth and depth of “understanding” aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with.