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

nlp vs nlu

We’ve seen that NLP primarily deals with analyzing the language’s structure and form, focusing on aspects like grammar, word formation, and punctuation. On the other hand, NLU is concerned with comprehending the deeper meaning and intention behind the language. Two fundamental concepts of NLU are intent recognition and entity recognition. Improvements in computing and machine learning have increased the power and capabilities of NLU over the past decade.

Systems can improve user experience and communication by using NLP’s language generation. NLP models can determine text sentiment—positive, negative, or neutral—using several methods. This analysis helps analyze public opinion, client feedback, social media sentiments, and other textual communication. Information retrieval, question-answering systems, sentiment analysis, and text summarization utilise NER-extracted data.

nlp vs nlu

While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities. 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. By leveraging machine learning and semantic analysis techniques, NLU enables machines to grasp the intricacies of human language. NLU algorithms leverage techniques like semantic analysis, syntactic parsing, and machine learning to extract relevant information from text or speech data and infer the underlying meaning. NLU goes beyond the basic processing of language and is meant to comprehend and extract meaning from text or speech.

With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5). At BioStrand, our mission is to enable an authentic systems biology approach to life sciences research, and natural language technologies play a central role in achieving that mission. Our LENSai Complex Intelligence Technology platform leverages the power of our HYFT® framework to organize the entire biosphere as a multidimensional network of 660 million data objects. Our proprietary bioNLP framework then integrates unstructured data from text-based information sources to enrich the structured sequence data and metadata in the biosphere. The platform also leverages the latest development in LLMs to bridge the gap between syntax (sequences) and semantics (functions).

Already applied in healthcare, education, marketing, advertising, software development, and finance, they actively permeate the human resources field. For example, for HR specialists seeking to hire Node.js developers, the tech can help optimize the search process to narrow down the choice to candidates with appropriate skills and programming language knowledge. Symbolic AI uses human-readable symbols that represent real-world entities or concepts. 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.

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. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue nlp vs nlu and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Questionnaires about people’s habits and health problems are insightful while making diagnoses.


Until recently, the idea of a computer that can understand ordinary languages and hold a conversation with a human had seemed like science fiction. This enables machines to produce more accurate and appropriate responses during interactions. Conversational interfaces are powered primarily by natural language processing (NLP), and a key subset of NLP is natural language understanding (NLU).

NLU focuses on understanding the meaning and intent of human language, while NLP encompasses a broader range of language processing tasks, including translation, summarization, and text generation. It also facilitates sentiment analysis, which involves determining the sentiment or emotion expressed in a piece of text, and information retrieval, where machines retrieve relevant information based on user queries. NLP has the potential to revolutionize industries such as healthcare, customer service, information retrieval, and language education, among others. NLP and NLU have made these possible and continue shaping the virtual communication field. Two subsets of artificial intelligence (AI), these technologies enable smart systems to grasp, process, and analyze spoken and written human language to further provide a response and maintain a dialogue. NLP consists of natural language generation (NLG) concepts and natural language understanding (NLU) to achieve human-like language processing.

With the advancements in machine learning, deep learning, and neural networks, we can expect to see even more powerful and accurate NLP, NLU, and NLG applications in the future. In essence, NLP focuses on the words that were said, while NLU focuses on what those words actually signify. Some users may complain about symptoms, others may write short phrases, and still, others may use incorrect grammar. Without NLU, there is no way AI can understand and internalize the near-infinite spectrum of utterances that the human language offers. Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings.

What’s the Difference Between Natural Language Processing and Natural Language Understanding?

For more information on the applications of Natural Language Understanding, and to learn how you can leverage Algolia’s search and discovery APIs across your site or app, please contact our team of experts. Like other modern phenomena such as social media, artificial intelligence has landed on the ecommerce industry scene with a giant … In conclusion, I hope now you have a better understanding of the key differences between NLU and NLP. This will empower your journey with confidence that you are using both terms in the correct context.

nlp vs nlu

Development of algorithms → Models are made → Enables computers to under → They easily interpret → Generate human-like language. Even website owners understand the value of this important feature and incorporate chatbots into their websites. They quickly provide answers to customer queries, give them recommendations, and do much more. Each plays a unique role at various stages of a conversation between a human and a machine. This book is for managers, programmers, directors – and anyone else who wants to learn machine learning.

To put it simply, NLP deals with the surface level of language, while NLU deals with the deeper meaning and context behind it. While NLP can be used for tasks like language translation, speech recognition, and text summarization, NLU is essential for applications like chatbots, virtual assistants, and sentiment analysis. NLP vs NLU comparisons help businesses, customers, and professionals understand the language processing and machine learning algorithms often applied in AI models.

Then, a dialogue policy determines what next step the dialogue system makes based on the current state. Finally, the NLG gives a response based on the semantic frame.Now that we’ve seen how a typical dialogue system works, let’s clearly understand NLP, NLU, and NLG in detail. The terms Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are often used interchangeably, but they have distinct differences.

nlp vs nlu

Historically, the first speech recognition goal was to accurately recognize 10 digits that were transmitted using a wired device (Davis et al., 1952). From 1960 onwards, numerical methods were introduced, and they were to effectively improve the recognition of individual components of speech, such as when you are asked to say 1, 2 or 3 over the phone. However, it will take much longer to tackle ‘continuous’ speech, which will remain rather complex for a long time (Haton et al., 2006). The aim is to analyze and understand a need expressed naturally by a human and be able to respond to it.

NLP models can learn language recognition and interpretation from examples and data using machine learning. These models are trained on varied datasets with many language traits and patterns. Basically, with this technology, the aim is to enable machines to understand and interpret human language.

  • By understanding their distinct strengths and limitations, businesses can leverage these technologies to streamline processes, enhance customer experiences, and unlock new opportunities for growth and innovation.
  • In practical applications such as customer support, recommendation systems, or retail technology services, it’s crucial to seamlessly integrate these technologies for more accurate and context-aware responses.
  • Both NLP and NLU are related but distinct fields within artificial intelligence that deal with the ability of computers to process and understand human language.
  • Finally, the NLG gives a response based on the semantic frame.Now that we’ve seen how a typical dialogue system works, let’s clearly understand NLP, NLU, and NLG in detail.
  • Natural Language Understanding provides machines with the capabilities to understand and interpret human language in a way that goes beyond surface-level processing.
  • With NLU models, however, there are other focuses besides the words themselves.

For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. These three terms are often used interchangeably but that’s not completely accurate. Natural language processing (NLP) is actually made up of natural language understanding (NLU) and natural language generation (NLG). Thus, it helps businesses to understand customer needs and offer them personalized products.

Which One Is Better: NLU or NLP?

The Marketing Artificial Intelligence Institute underlines how important all of this tech is to the future of content marketing. One of the toughest challenges for marketers, one that we address in several posts, is the ability to create content at scale. You may then ask about specific stocks you own, and the process starts all over again. It takes your question and breaks it down into understandable pieces – “stock market” and “today” being keywords on which it focuses. When dealing with speech interaction, it is essential to define a real-time transcription system for speech interaction. Hiren is VP of Technology at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation.

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.

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. Since NLU can understand advanced and complex sentences, it is used to create intelligent assistants and provide text filters. For instance, it helps systems like Google Translate to offer more on-point results that carry over the core intent from one language to another. Without NLP, the computer will be unable to go through the words and without NLU, it will not be able to understand the actual context and meaning, which renders the two dependent on each other for the best results. Therefore, the language processing method starts with NLP but gradually works into NLU to increase efficiency in the final results.

If you give an idea to an NLG system, the system synthesizes and transforms that idea into a sentence. It uses a combinatorial process of analytic output and contextualized outputs to complete these tasks. But before any of this natural language processing can happen, the text needs to be standardized. The entity is a piece of information present in the user’s request, which is relevant to understand their objective. It is typically characterized by short words and expressions that are found in a large number of inputs corresponding to the same objective. Natural Language Understanding (NLU) refers to the analysis of a written or spoken text in natural language and understanding its meaning.

When we hear or read  something our brain first processes that information and then we understand it. That is because we can’t process all information – we can only process information that is within our familiar realm. Together they are shaping the future of human-computer interaction and communication. It’s important to be updated regarding these changes and innovations in the world so you can use these natural language capabilities to their fullest potential for your business success.

It is a subset ofNatural Language Processing (NLP), which also encompasses syntactic and pragmatic analysis, as well as discourse processing. As NLP algorithms become more sophisticated, chatbots and virtual assistants are providing seamless and natural interactions. Meanwhile, improving NLU capabilities enable voice assistants to understand user queries more accurately.

Natural Language Processing (NLP) vs Natural Language Understanding (NLU): Explore the Differences

Data pre-processing aims to divide the natural language content into smaller, simpler sections. ML algorithms can then examine these to discover relationships, connections, and context between these smaller sections. NLP links Paris to France, Arkansas, and Paris Hilton, as well as France to France and the French national football team.

Together, NLU and natural language generation enable NLP to function effectively, providing a comprehensive language processing solution. Entity recognition, intent recognition, sentiment analysis, contextual understanding, etc. The algorithms utilized in NLG play a vital role in ensuring the generation of coherent and meaningful language. They analyze the underlying data, determine the appropriate structure and flow of the text, select suitable words and phrases, and maintain consistency throughout the generated content.

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. Instead, machines must know the definitions of words and sentence structure, along with syntax, sentiment and intent. It’s a subset of NLP and It works within it to assign structure, rules and logic to language so machines can “understand” what is being conveyed in the words, phrases and sentences in text. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability.

SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items. ATNs and their more general format called «generalized ATNs» continued to be used for a number of years. A Voice Assistant is an AI-infused software entity designed to interpret and respond to voice commands for users interact with through spoken language. Just like learning to read where you first learn the alphabet, then sounds, and eventually words, the transcription of speech has evolved over time with technology. You can foun additiona information about ai customer service and artificial intelligence and NLP. Its main purpose is to allow machines to record and process information in natural language. When an unfortunate incident occurs, customers file a claim to seek compensation.

  • Now, consider that this task is even more difficult for machines, which cannot understand human language in its natural form.
  • In this section, we will introduce the top 10 use cases, of which five are related to pure NLP capabilities and the remaining five need for NLU to assist computers in efficiently automating these use cases.
  • A natural language is a language used as a native tongue by a group of speakers, such as English, Spanish, Mandarin, etc.
  • NLP full form is Natural Language Processing (NLP) is an exciting field that focuses on enabling computers to understand and interact with human language.

By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Sentiment analysis, thus NLU, can locate fraudulent reviews by identifying the text’s emotional character. For instance, inflated statements and an excessive amount of punctuation may indicate a fraudulent review. In this section, we will introduce the top 10 use cases, of which five are related to pure NLP capabilities and the remaining five need for NLU to assist computers in efficiently automating these use cases.

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). This intent recognition concept is based on multiple algorithms drawing from various texts to understand sub-contexts and hidden meanings. With NLP, the main focus is on the input text’s structure, presentation and syntax. It will extract data from the text by focusing on the literal meaning of the words and their grammar.

Knowledge-Enhanced biomedical language models have proven to be more effective at knowledge-intensive BioNLP tasks than generic LLMs. In 2020, researchers created the Biomedical Language Understanding and Reasoning Benchmark (BLURB), a comprehensive benchmark and leaderboard to accelerate the development of biomedical NLP. To have a clear understanding of these crucial language processing concepts, let’s explore the differences between NLU and NLP by examining their scope, purpose, applicability, and more. As with NLU, NLG applications need to consider language rules based on morphology, lexicons, syntax and semantics to make choices on how to phrase responses appropriately. Human language, verbal or written, is very ambiguous for a computer application/code to understand. These examples are a small percentage of all the uses for natural language understanding.

NLP vs. NLU: from Understanding a Language to Its Processing – KDnuggets

NLP vs. NLU: from Understanding a Language to Its Processing.

Posted: Wed, 03 Jul 2019 07:00:00 GMT [source]

Anything you can think of where you could benefit from understanding what natural language is communicating is likely a domain for NLU. If you only have NLP, then you can’t interpret the meaning of a sentence or phrase. Without NLU, your system won’t be able to respond appropriately in natural language.

NLU is also utilized in sentiment analysis to gauge customer opinions, feedback, and emotions from text data. Additionally, it facilitates language understanding in voice-controlled devices, making them more intuitive and user-friendly. NLU is at the forefront of advancements in AI and has the potential to revolutionize areas such as customer service, personal assistants, content analysis, and more. 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 (Natural Language Understanding) is mainly concerned with the meaning of language, so it doesn’t focus on word formation or punctuation in a sentence. Instead, its prime objective is to bring out the actual intent of the speaker by analysing the different possible contexts of every sentence. With NLU models, however, there are other focuses besides the words themselves. These algorithms aim to fish out the user’s real intent or what they were trying to convey with a set of words.

Natural language processing is a subset of AI, and it involves programming computers to process massive volumes of language data. It involves numerous tasks that break down natural language into smaller elements in order to understand the relationships between those elements and how they work together. Common tasks include parsing, speech recognition, part-of-speech tagging, and information extraction.

However, if a developer wants to build an intelligent contextual assistant capable of having sophisticated natural-sounding conversations with users, they would need NLU. NLU is the component that allows the contextual assistant to understand the intent of each utterance by a user. Without it, the assistant won’t be able to understand what a user means throughout a conversation. And if the assistant doesn’t understand what the user means, it won’t respond appropriately or at all in some cases. The earliest language models were rule-based systems that were extremely limited in scalability and adaptability.

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. Technology will continue to make NLP more accessible for both businesses and customers.

For example, an NLG system might be used to generate product descriptions for an e-commerce website or to create personalized email marketing campaigns. Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. 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. 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.

Furthermore, based on specific use cases, we will investigate the scenarios in which favoring one skill over the other becomes more profitable for organizations. This research will provide you with the insights you need to determine which AI solutions are most suited to your organization’s specific needs. NLP allows us to resolve ambiguities in language more quickly and adds structure to the collected data, which are then used by other systems. NLP deals with language structure, and NLU deals with the meaning of language. It also helps in eliminating any ambiguity or confusion from the conversation.

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