NLP vs NLU: Whats The Difference? BMC Software Blogs

When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills. With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5).

Natural Language Understanding (NLU) refers to the analysis of a written or spoken text in natural language and understanding its meaning. NLP or ‘Natural Language Processing’ is a set of text recognition solutions that can understand words and sentences formulated by users. The difference between them is that NLP can work with just about any type of data, whereas NLU is a subset of NLP and is just limited to structured data. In other words, NLU can use dates and times as part of its conversations, whereas NLP can’t. Only 20% of data on the internet is structured data and usable for analysis.

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. NLU, on the other hand, is more concerned with the higher-level understanding. It aims to highlight appropriate information, guess context, and take actionable insights from the given text or speech data. The tech builds upon the foundational elements of NLP but delves deeper into semantic and contextual language comprehension. These approaches are also commonly used in data mining to understand consumer attitudes.

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It all comes down to breaking down the primary language we use every day, and it has been used across many products for many years now. Some common examples of NLP applications include editing software, search engines, chatbots, text summarisation, categorization, mining, and even part-of-speech tagging. It enables computers to evaluate and organize unstructured text or speech input in a meaningful way that is equivalent to both spoken and written human language. Instead, machines must know the definitions of words and sentence structure, along with syntax, sentiment and intent. Natural language understanding (NLU) is concerned with the meaning of words. 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.

As technology progresses, we can expect more nuanced and sophisticated tools in each of these domains, further blurring the lines between human and machine communication. Semantically, it looks for the true meaning behind the words by comparing them to similar examples. At the same time, it breaks down text into parts of speech, sentence structure, and morphemes (the smallest understandable part of a word).

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

NLU is concerned with understanding the text so that it can be processed later. NLU is specifically scoped to understanding text by extracting meaning from it in a machine-readable way for future processing. Because NLU encapsulates processing of the text alongside understanding it, NLU is a discipline within NLP..

It’s also changing how users discover content, from what they search for on Google to what they binge-watch on Netflix. The transcription uses algorithms called Automatic Speech Recognition (ASR), which generates a written version of the conversation in real time. 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.

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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. NLU is the ability of a machine to understand and process the meaning of speech or text presented in a natural language, that is, the capability to make sense of natural language.

They cater to various industries, offering specialized solutions like Appic for app development and Hifly SODA for sales-oriented analytics. EDLIGO has a strong track record, with customers successfully using our platform in more than twenty countries, boasting more than 2 million users, and filing 17 patents. In 2023, EDLIGO was recognized as one of Germany’s top three most innovative mid-sized companies in software.

Processing big data involved with understanding the spoken language is comparatively easier and the nets can be trained to deal with uncertainty, without explicit programming. The callbot powered by artificial intelligence difference between nlp and nlu has an advanced understanding of natural language because of NLU. If this is not precise enough, human intervention is possible using a low-code conversational agent creation platform for instance.

By automating and streamlining these aspects, Paychex helps businesses save time and reduce errors. They cater to small and mid-sized businesses, providing tools for tax administration, employee onboarding, and regulatory compliance. The platform is useful for threat intelligence, risk mitigation, and fraud detection. It’s valuable to government, law enforcement, and commercial sectors for its ability to process and analyze large volumes of data, helping them stay ahead of threats and risks. Babel Street’s platform empowers organizations with AI-driven insights from vast public and commercial data sources.

By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Since it is not a standardized conversation, NLU capabilities are required. For instance, the address of the home a customer wants to cover has an impact on the underwriting process since it has a relationship with burglary risk.

That means there are no set keywords at set positions when providing an input. As the largest minority insights company, Shapiro+Raj operates with an integrated team comprising social scientists, data analysts, brand strategists, and creative ideators. Their approach combines social science and behavioral economics, enhanced by a blend of technology and humanity. CEE partners with over 950 schools in 280 districts, offering services like strategic planning, coaching, professional development, and research projects. Key benefits include improved customer experience, reduced handle times, enhanced agent performance, and increased revenue opportunities.

Key Components Of NLP

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. 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 analyzing the different possible contexts of every sentence. 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.

Everyone can ask questions and give commands to what is perceived as an “omniscient” chatbot. Big Tech got shaken up with Google introducing their LaMDA-based “Bard” and Bing Search incorporating GPT-4 with Bing Chat. We discussed this with Arman van Lieshout, Product Manager at CM.com, for our Conversational AI solution.

However, as discussed in this guide, NLU (Natural Language Understanding) is just as crucial in AI language models, even though it is a part of the broader definition of NLP. Both these algorithms are essential in handling complex human language and giving machines the input that can help them devise better solutions for the end user. Intent recognition and sentiment analysis are the main outcomes of the NLU. Thus, it helps businesses to understand customer needs and offer them personalized products.

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. Conversational AI employs natural language understanding, machine learning, and natural language processing to engage in customer conversations.

For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc. NLU algorithms often operate on text that has already been standardized by text pre-processing steps. Sotech offers comprehensive research services including product testing, strategy consulting, message testing, and brand health tracking. They cater to various industries like consumer products, financial services, restaurants, and technology.

Understanding AI methodology is essential to ensuring excellent outcomes in any technology that works with human language. Hybrid natural language understanding platforms combine multiple approaches—machine learning, deep learning, LLMs and symbolic or knowledge-based AI. They improve the accuracy, scalability and performance of NLP, NLU and NLG technologies. With AI and machine learning (ML), NLU(natural language understanding), NLP ((natural language processing), and NLG (natural language generation) have played an essential role in understanding what user wants.

Thus, we need AI embedded rules in NLP to process with machine learning and data science. Machines help find patterns in unstructured data, which then help people in understanding the meaning of that data. Suppose companies wish to implement AI systems that can interact with users without direct supervision. In that case, it is essential to ensure that machines can read the word and grasp the actual meaning. This helps the final solution to be less rigid and have a more personalized touch. 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.

• Paychex offers a range of services aimed at simplifying payroll and HR processes for businesses.
• With more progress in technology made in recent years, there has also emerged a new branch of artificial intelligence, other than NLP and NLU.
• His current active areas of research are conversational AI and algorithmic bias in AI.
• One of the primary goals of NLU is to teach machines how to interpret and understand language inputted by humans.

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 a branch of artificial intelligence (AI) that bridges human and machine language to enable more natural human-to-computer communication. When information goes into a typical NLP system, it goes through various phases, including lexical analysis, discourse integration, pragmatic analysis, parsing, and semantic analysis.

Such tasks can be automated by an NLP-driven hospitality chatbot (see Figure 7). Both technologies are widely used across different industries and continue expanding. 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. To pass the test, a human evaluator will interact with a machine and another human at the same time, each in a different room.

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Their unique approach combines advanced qualitative and quantitative methods, focusing on the critical 10% of new information essential for business growth. CEE’s approach centers on understanding and improving school climate and culture, enhancing performance, and promoting continuous improvement. The Center for Educational Effectiveness (CEE) specializes in developing surveys, data tools, and services to support the growth of communities, districts, schools, and individuals. They focus on creating a positive impact in the educational sector since 1999. Clarteza focuses on driving brand innovation by deeply understanding consumer behavior and needs.

This creates a black box where data goes in, decisions go out, and there is limited visibility into how one impacts the other. What’s more, a great deal of computational power is needed to process the data, while large volumes of data are required to both train and maintain a model. In AI, two main branches play a vital role in enabling machines to understand human languages and perform the necessary functions. Grammar and the literal meaning of words pretty much go out the window whenever we speak. It is easy to see why natural language understanding is an extremely important issue for companies that want to use intelligent robots to communicate with their customers.

As a result, they do not require both excellent NLU skills and intent recognition. 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. Whether it’s simple chatbots or sophisticated AI assistants, NLP is an integral part of the conversational app building process.

Their tools cater to various data types including e-commerce products, job postings, news articles, and real estate listings, ensuring high-quality data extraction. If you’re finding the answer to this question, then the truth is that there’s no definitive answer. Both of these fields offer various benefits that can be utilized to make better machines. This machine doesn’t just focus on grammatical structure but highlights necessary information, actionable insights, and other essential details. You’ll probably use Alexa or Siri to make the process smoother and save time.

NLP focuses on processing the text in a literal sense, like what was said. Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant. From the computer’s point of view, any natural language is a free form text.

While excellent customer service is an essential focus of any successful brand, forward-thinking companies are forming customer-focused multidisciplinary teams to help create exceptional customer experiences. This algorithmic approach uses statistical analysis of ‘training’ documents to establish rules and build its knowledge base. However, because language and grammar rules can be complex and contradictory, this algorithmic approach can sometimes produce incorrect results without human oversight and correction. Using a set of linguistic guidelines coded into the platform that use human grammatical structures. However, this approach requires the formulation of rules by a skilled linguist and must be kept up-to-date as issues are uncovered. This can drain resources in some circumstances, and the rule book can quickly become very complex, with rules that can sometimes contradict each other.

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.

His expertise in building scalable and robust tech solutions has been instrumental in the company’s growth and success. NLU relies on NLP’s syntactic analysis to detect and extract the structure and context of the language, which is then used to derive meaning and understand intent. Processing techniques serve as the groundwork upon which understanding techniques are developed and applied. NLP utilizes statistical models and rule-enabled systems to handle and juggle with language. It often relies on linguistic rules and patterns to analyze and generate text. Handcrafted rules are designed by experts and specify how certain language elements should be treated, such as grammar rules or syntactic structures.

It is another subfield of NLP called NLG, or Natural Language Generation, which has received a lot of prominence and recognition in recent times. Imagine you had a tool that could read and interpret content, find its strengths and its flaws, and then write blog posts that meet the needs of both search engines and your users. NLP and NLU will analyze content on the stock market and break it down, while NLG will take the applicable data and turn it into a templated story for your site.

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. 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. NLP and NLU are significant terms for designing a machine that can easily understand the human language, whether it contains some common flaws. As a seasoned technologist, Adarsh brings over 14+ years of experience in software development, artificial intelligence, and machine learning to his role.

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. Pursuing the goal to create a chatbot that can hold a conversation with humans, researchers are developing chatbots that will be able to process natural language. Natural language processing works by taking unstructured text and converting it into a correct format or a structured text. 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.

• The difference between them is that NLP can work with just about any type of data, whereas NLU is a subset of NLP and is just limited to structured data.
• 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.
• Sometimes you may have too many lines of text data, and you have time scarcity to handle all that data.

Their AI-driven approach ensures efficient data handling, personalized customer interactions, and streamlined operations. It’ll help create a machine that can interact with humans and engage with them in an engaging way. Remember that using the right technique for your project is crucial to its success. These handcrafted rules are made in a way that ensures the machine understands how to connect each element. It doesn’t just do basic processing; instead, it comprehends and then extracts meaning from your data. Development of algorithms → Models are made → Enables computers to under → They easily interpret → Generate human-like language.

They say percentages don’t matter in life, but in marketing, they are everything. The customer journey, from acquisition to retention, is filled with potential incremental drop-offs at every touchpoint. A confusing experience here, an ill-timed communication there, and your conversion rate is suddenly plummeting. It allows callers to interact with an automated assistant without the need to speak to a human and resolve issues via a series of predetermined automated questions and responses. However, there are still many challenges ahead for NLP & NLU in the future.

But it can actually free up editorial professionals by taking on the rote tasks of content creation and allowing them to create the valuable, in-depth content for which your visitors are searching. It will use NLP and NLU to analyze your content at the individual or holistic level. While it can’t write entire blog posts for you, it can generate briefs that cover all the questions that should be answered, the keywords that should appear, and the internal and external links that should be included. NLP is also used whenever you ask Alexa, Siri, Google, or Cortana a question, and anytime you use a chatbot.

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). By embracing the differences and pushing the boundaries of language understanding, we can shape a future where machines truly comprehend and communicate with humans in an authentic and effective way. When it comes to natural language, what was written or spoken may not be what was meant. In the most basic terms, NLP looks at what was said, and NLU looks at what was meant.