NLU vs NLP in 2024: Main Differences & Use Cases Comparison

Using NLU technology, you can sort unstructured data (email, social media, live chat, etc.) by topic, sentiment, and urgency (among others). These tickets can then be routed directly to the relevant agent and prioritized. 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.

In AI, two main branches play a vital role in enabling machines to understand human languages and perform the necessary functions. 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. Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings. Natural language understanding systems let organizations create products or tools that can both understand words and interpret their meaning.

One key characteristic of ML is the ability to help computers improve their performance over time without explicit programming, making it well-suited for task automation. For more advanced knowledge, start with Andrew Ng’s Machine Learning Specialization for a broad introduction to the concepts of machine learning. Next, build and train artificial neural networks in the Deep Learning Specialization. At its most basic level, the field of artificial intelligence uses computer science and data to enable problem solving in machines.

Another difference is that NLP breaks and processes language, while NLU provides language comprehension. Watch IBM Data and AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. At Kommunicate, we envision a world-beating customer support solution to empower the new era of customer support. We would love to have you on board to have a first-hand experience of Kommunicate. Automated reasoning is a subfield of cognitive science that is used to automatically prove mathematical theorems or make logical inferences about a medical diagnosis.

In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further. As NLG algorithms become more sophisticated, they can generate more natural-sounding and engaging content.

Natural Language Processing focuses on the interaction between computers and human language. It involves the development of algorithms and techniques to enable computers to comprehend, analyze, and generate textual or speech input in a meaningful and useful way. The tech aims at bridging the gap between human interaction and computer understanding. 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. Importantly, though sometimes used interchangeably, they are actually two different concepts that have some overlap.

At this point, there comes the requirement of something called ‘natural language’ in the world of artificial intelligence. Thus, we need AI embedded rules in NLP to process with machine learning and data science. This intent recognition concept is based on multiple algorithms drawing from various texts to understand sub-contexts and hidden meanings.

This technology brings us closer to a future where machines can truly understand and interact with us on a deeper level. Natural language processing refers to the ability of computer systems to analyze, understand, and derive meaning from human language. NLP algorithms use linguistic rules and machine learning techniques to process natural language data.

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With more progress in technology made in recent years, there has also emerged a new branch of artificial intelligence, other than NLP and NLU. It is another subfield of NLP called NLG, or Natural Language Generation, which has received a lot of prominence and recognition in recent times. In recent years, with so many advancements in research and technology, companies and industries worldwide have opted for the support of Artificial Intelligence (AI) to speed up and grow their business. AI uses the intelligence and capabilities of humans in software and programming to boost efficiency and productivity in business.

In addition, it can add a touch of personalization to a digital product or service as users can expect their machines to understand commands even when told so in natural language. Both language processing algorithms are used by multiple businesses across several different industries. For example, NLP is often used for SEO purposes by businesses since the information extraction feature can draw up data related to any keyword. By accessing the storage of pre-recorded results, NLP algorithms can quickly match the needed information with the user input and return the result to the end-user in seconds using its text extraction feature. NLP algorithms are used to understand the meaning of a user’s text in a machine, while NLU algorithms take actions and core decisions.

NLU vs NLP in 2024: Main Differences & Use Cases Comparison

For example, with watsonx and Hugging Face AI builders can use pretrained models to support a range of NLP tasks. 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. By way of contrast, NLU targets deep semantic understanding and multi-faceted analysis to comprehend the meaning, aim, and textual environment. NLU techniques enable systems to grasp the nuances, references, and connections within the text or speech resolve ambiguities and incorporate external knowledge for a comprehensive understanding. Technology continues to advance and contribute to various domains, enhancing human-computer interaction and enabling machines to comprehend and process language inputs more effectively. NLP and NLU have unique strengths and applications as mentioned above, but their true power lies in their combined use.

NLG systems enable computers to automatically generate natural language text, mimicking the way humans naturally communicate — a departure from traditional computer-generated text. When given a natural language nlp vs nlu input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols. The tokens are run through a dictionary that can identify a word and its part of speech.

The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning. In summary, while NLP focuses on processing natural language based on linguistic rules, NLU is about achieving genuine understanding and reasoning like humans. NLP employs both rule-based systems and statistical models to analyze and generate text. Linguistic patterns and norms guide rule-based approaches, where experts manually craft rules for handling language components like syntax and grammar. NLP’s dual approach blends human-crafted rules with data-driven techniques to comprehend and generate text effectively.

With natural language processing, computers can analyze the text put in by the user. In contrast, natural language understanding tries to understand the user’s intent and helps match the correct answer based on their needs. Learn how to extract and classify text from unstructured data with MonkeyLearn’s no-code, low-code text analysis tools.

These components are the building blocks that work together to enable chatbots to understand, interpret, and generate natural language data. By leveraging these technologies, chatbots can provide efficient and effective customer service and support, freeing up human agents to focus on more complex tasks. NLP vs NLU comparisons help businesses, customers, and professionals understand the language processing and machine learning algorithms often applied in AI models.

Human language is filled with many ambiguities that make it difficult for programmers to write software that accurately determines the intended meaning of text or voice data. Human language might take years for humans to learn—and many never stop learning. But then programmers must teach natural language-driven applications to recognize and understand irregularities so their applications can be accurate and useful.

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. 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. It involves tasks like entity recognition, intent recognition, and context management.

NLG is used to generate a semantic understanding of the original document and create a summary through text abstraction or text extraction. In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content. Text abstraction, the original document is phrased in a linguistic way, text interpreted and described using new concepts, but the same information content is maintained. Natural language processing works by taking unstructured text and converting it into a correct format or a structured text.

Recommendations on Spotify or Netflix, auto-correct and auto-reply, virtual assistants, and automatic email categorization, to name just a few. By default, virtual assistants tell you the weather for your current location, unless you specify a particular city. The goal of question answering is to give the user response in their natural language, rather than a list of text answers. Try out no-code text analysis tools like MonkeyLearn to  automatically tag your customer service tickets. Simply put, using previously gathered and analyzed information, computer programs are able to generate conclusions.

It works by building the algorithm and training the model on large amounts of data analyzed to understand what the user means when they say something. Also, NLP processes a large amount of human data and focus on use of machine learning and deep learning techniques. Natural Language is an evolving linguistic system shaped by usage, as seen in languages like Latin, English, and Spanish. Conversely, constructed languages, exemplified by programming languages like C, Java, and Python, follow a deliberate development process. Natural Language Processing (NLP), a facet of Artificial Intelligence, facilitates machine interaction with these languages.

In machine learning (ML) jargon, the series of steps taken are called data pre-processing. 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. As humans, we can identify such underlying similarities almost effortlessly https://chat.openai.com/ 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. 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.

Thus, NLP models can conclude that “Paris is the capital of France” sentence refers to Paris in France rather than Paris Hilton or Paris, Arkansas. In addition to processing natural language similarly to a human, NLG-trained machines are now able to generate new natural language text—as if written by another human. All this has sparked a lot of interest both from commercial adoption and academics, making NLP one of the most active research topics in AI today.

How can companies use NLP and NLU together?

Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use daily. While each technology has its own unique set of applications and use cases, the lines between them are becoming increasingly blurred as they continue to evolve and converge. 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. However, when it comes to handling the requests of human customers, it becomes challenging. This is due to the fact that with so many customers from all over the world, there is also a diverse range of languages.

• NLG is the process of producing a human language text response based on some data input.
• NLU, however, understands the idiom and interprets the user’s intent as being hungry and searching for a nearby restaurant.
• This technology is used in applications like automated report writing, customer service, and content creation.
• While it is true that NLP and NLU are often used interchangeably to define how computers work with human language, we have already established the way they are different and how their functions can sometimes submerge.

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 consume daily, social media, news, research, product websites, and more. Collecting and labeling that data can be costly and time-consuming for businesses.

What is the Future of Natural Language?

Developers need to understand the difference between natural language processing and natural language understanding so they can build successful conversational applications. Machine learning (ML) is an integral field that has driven many AI advancements, including key developments in natural language processing (NLP). While there is some overlap between ML and NLP, each field has distinct capabilities, use cases and challenges. Another area of advancement in NLP, NLU, and NLG is integrating these technologies with other emerging technologies, such as augmented and virtual reality. As these technologies continue to develop, we can expect to see more immersive and interactive experiences that are powered by natural language processing, understanding, and generation. Similarly, NLU is expected to benefit from advances in deep learning and neural networks.

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. They analyze context, semantics, sentiments, intents, and the nuances of human language. Over the past decade, NLP has advanced significantly owing to neural networks and deep learning. From simple rule-based systems, NLP has graduated to context-aware AI models like BERT and GPT-3 that can understand language almost like humans. However, the grammatical correctness or incorrectness does not always correlate with the validity of a phrase.

This allowed it to provide relevant content for people who were interested in specific topics. This allowed LinkedIn to improve its users’ experience and enable them to get more out of their platform. NLU recognizes that language is a complex task made up of many components such as motions, facial expression recognition etc. Furthermore, NLU enables computer programmes to deduce purpose from language, even if the written or spoken language is flawed.

NLP is the more traditional processing system, whereas NLU is much more advanced, even as a subset of the former. Since it would be challenging to analyze text using just NLP properly, the solution is coupled with NLU to provide sentimental analysis, which offers more precise insight into the actual meaning of the conversation. Online retailers can use this system to analyze the meaning of feedback on their product pages and primary site to understand if their clients are happy with their products. However, syntactic analysis is more related to the core of NLU examples, where the literal meaning behind a sentence is assessed by looking into its syntax and how words come together. Since NLU can understand advanced and complex sentences, it is used to create intelligent assistants and provide text filters.

NLP vs NLU Use Cases Comparison

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. On the other hand, natural language understanding is concerned with semantics – the study of meaning in language. NLU techniques such as sentiment analysis and sarcasm detection allow machines to decipher the true meaning of a sentence, even when it is obscured by idiomatic expressions or ambiguous phrasing. With the help of natural language understanding (NLU) and machine learning, computers can automatically analyze data in seconds, saving businesses countless hours and resources when analyzing troves of customer feedback.

When given just an answer, the machine was programmed to come up with the matching question. This allowed Watson to modify its algorithms, or in a sense “learn” from its mistakes. You can foun additiona information about ai customer service and artificial intelligence and NLP. While we don’t yet have human-like robots trying to take over the world, we do have examples of AI all around us. These could be as simple as a computer program that can play chess, or as complex as an algorithm that can predict the RNA structure of a virus to help develop vaccines.

In this case, NLU can help the machine understand the contents of these posts, create customer service tickets, and route these tickets to the relevant departments. This intelligent robotic assistant can also learn from past customer conversations and use this information to improve future responses. NLP centers on processing and manipulating language for machines to understand, interpret, and generate natural language, emphasizing human-computer interactions. Its core objective is furnishing computers with methods and algorithms for effective processing and modification of spoken or written language.

Machines programmed with NGL help in generating new texts in addition to the already processed natural language. They are so advanced and innovative that they appear as if a real human being has written them. As shown via these examples, choosing NLP vs NLU depends on whether the use case requires processing large volumes of text data or understanding nuanced human conversations. NLU allows understanding the context and emotions of customers filing claims to speed up processing and settlement. For many real-world applications, using NLP and NLU together provides the best results. However, NLP still lacks true comprehension of natural language and is prone to errors in ambiguity.

For example, NLP can extract the key product features from catalog descriptions to enable faceted search and filters. If a customer searches for “black leather jackets”, NLP can understand the color, material, and product Chat GPT type. NLP can analyze product titles, descriptions, attributes, and categories to improve search relevancy. NLU shines for use cases that require subjective comprehension of natural language and its many subtleties.

It starts with NLP (Natural Language Processing) at its core, which is responsible for all the actions connected to a computer and its language processing system. This involves receiving human input, processing it, and putting out a response. In addition to natural language understanding, natural language generation is another crucial part of NLP. While NLU is responsible for interpreting human language, NLG focuses on generating human-like language from structured and unstructured data.

In human language processing, NLP and NLU, while visually resembling each other, serve distinct functions. Examining “NLU vs NLP” reveals key differences in four crucial areas, highlighting the nuanced disparities between these technologies in language interpretation. Natural language understanding is the leading technology behind intent recognition. It is mainly used to build chatbots that can work through voice and text and potentially replace human workers to handle customers independently.

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. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. AI technology has become fundamental in business, whether you realize it or not.

NLU goes beyond the basic processing of language and is meant to comprehend and extract meaning from text or speech. As a result, NLU  deals with more advanced tasks like semantic analysis, coreference resolution, and intent recognition. As we continue to advance in the realms of artificial intelligence and machine learning, the importance of NLP and NLU will only grow. However, navigating the complexities of natural language processing and natural language understanding can be a challenging task.

Structured data is important for efficiently storing, organizing, and analyzing information. NLU focuses on understanding human language, while NLP covers the interaction between machines and natural language. 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. Before a computer can process unstructured text into a machine-readable format, first machines need to understand the peculiarities of the human language. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding.

In just 6 hours, you’ll gain foundational knowledge about AI terminology, strategy, and the workflow of machine learning projects. Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems. 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. NLU enables travel chatbots to have natural conversations with users, understand their needs and preferences, and provide personalized recommendations. For example, a customer describing an accident and injuries can be analyzed with NLU to estimate claim validity and severity for fair payouts. NLU allows understanding customer needs and preferences from their browsing history and interactions.