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

Chatbots offer 24-7 support and are excellent problem-solvers, often providing instant solutions to customer inquiries. These low-friction channels allow customers to quickly interact with your organization with little hassle. Where NLP helps machines read and process text and NLU helps them understand text, NLG or Natural Language Generation helps machines write text.

Manual ticketing is a tedious, inefficient process that often leads to delays, frustration, and miscommunication. This technology allows your system to understand the text within each ticket, effectively filtering and routing tasks to the appropriate expert or department. For example, it is difficult for call center employees to remain consistently positive with customers at all hours of the day or night. However, a chatbot can maintain positivity and safeguard your brand’s reputation. By 2025, the NLP market is expected to surpass $43 billion–a 14-fold increase from 2017. Businesses worldwide are already relying on NLU technology to make sense of human input and gather insights toward improved decision-making.

How does NLU work?

Word sense disambiguation often makes use of part of speech taggers in order to contextualize the target word. Supervised methods of word-sense disambiguation include the user of support vector machines and memory-based learning. However, most word sense disambiguation models are semi-supervised models that employ both labeled and unlabeled data. For instance, virtual assistants like Siri, Alexa, and Google Assistant use NLU to understand and respond to voice commands. Additionally, NLU is used in text analysis, sentiment analysis, and machine translation.

Natural Language Understanding (NLU) has become an essential part of many industries, including customer service, healthcare, finance, and retail. NLU technology enables computers and other devices to understand and interpret human language by analyzing and processing the words and syntax used in communication. This has opened up countless possibilities and applications for NLU, ranging from chatbots to virtual assistants, and even automated customer service.

Intent recognition

NLG can be used to generate natural language summaries of data or to generate natural language instructions for a task such as how to set up a printer. Natural Language Understanding is a big component of IVR since interactive voice response is taking in someone’s words and processing it to understand the intent and sentiment behind the caller’s needs. IVR makes a great impact on customer support teams that utilize phone systems as a channel since it can assist in mitigating support needs for agents. Natural Language Understanding (NLU) refers to the process by which machines are able to analyze, interpret, and generate human language. Furthermore, different languages have different grammatical structures, which could also pose challenges for NLU systems to interpret the content of the sentence correctly.

As a rule of thumb, an algorithm that builds a model that understands meaning falls under natural language understanding, not just natural language processing. Alexa is exactly that, allowing users to input commands through voice instead of typing them in. Therefore, NLU can be used for anything from internal/external email responses and chatbot discussions to social media comments, voice assistants, IVR systems for calls and internet search queries. Parsing is merely a small aspect of natural language understanding in AI – other, more complex tasks include semantic role labelling, entity recognition, and sentiment analysis. Statistical models use machine learning algorithms such as deep learning to learn the structure of natural language from data.

Natural Language Processing (NLP): 7 Key Techniques

That makes it possible to do things like content analysis, machine translation, topic modeling, and question answering on a scale that would be impossible for humans. Your software can take a statistical sample of recorded calls and perform speech recognition after transcribing the calls to text using machine translation. The NLU-based text analysis can link specific speech patterns to negative emotions and high effort levels. Using predictive modeling algorithms, you can identify these speech patterns automatically in forthcoming calls and recommend a response from your customer service representatives as they are on the call to the customer. This reduces the cost to serve with shorter calls, and improves customer feedback. John Ball, cognitive scientist and inventor of Patom Theory, supports this assessment.

Natural Language Understanding deconstructs human speech using trained algorithms until it forms a structured ontology, or a set of concepts and categories that have established relationships with one another. This computational linguistics data model is then applied to text or speech as in the example above, first identifying key parts of the language. Natural Language Understanding is a subset area of research and development that relies on foundational elements from Natural Language Processing (NLP) systems, which nlu meaning map out linguistic elements and structures. Natural Language Processing focuses on the creation of systems to understand human language, whereas Natural Language Understanding seeks to establish comprehension. 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. When given a natural language input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols.

Some of the capabilities your NLU technology should have

For example, NLU can be used to identify and analyze mentions of your brand, products, and services. This can help you identify customer pain points, what they like and dislike about your product, and what features they would like to see in the future. 5 min read – With new tools and technologies in hand, organizations can find new ways to use it to reach their own goals—and a more sustainable future.

NLU is a subset of NLP that teaches computers what a piece of text or spoken speech means. NLU leverages AI to recognize language attributes such as sentiment, semantics, context, and intent. Using NLU, computers can recognize the many ways in which people are saying the same things. Botpress can be used to build simple chatbots as well as complex conversational language understanding projects. The platform supports 12 languages natively, including English, French, Spanish, Japanese, and Arabic. Language capabilities can be enhanced with the FastText model, granting users access to 157 different languages.

In order to categorize or tag texts with humanistic dimensions such as emotion, effort, intent, motive, intensity, and more, Natural Language Understanding systems leverage both rules based and statistical machine learning approaches. 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. NLU or Natural Language Understanding is a subfield of Artificial Intelligence (AI) that focuses on the interaction between humans and computers using natural language.

A Voice Assistant is an AI-infused software entity designed to interpret and respond to voice commands for users interact with through spoken language. Natural Language Processing (NLP) is a branch of computer science that enables machines to interpret and comprehend human language for various tasks. Akkio is an easy-to-use machine learning platform that provides a suite of tools to develop and deploy NLU systems, with a focus on accuracy and performance. Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. NLU technology can also help customer support agents gather information from customers and create personalized responses.

Usage and Context

Artificial intelligence is critical to a machine’s ability to learn and process natural language. So, when building any program that works on your language data, it’s important to choose the right AI approach. This is in contrast to NLU, which applies grammar rules (among other techniques) to “understand” the meaning conveyed in the text.

It should be able  to understand complex sentiment and pull out emotion, effort, intent, motive, intensity, and more easily, and make inferences and suggestions as a result. Entity recognition identifies which distinct entities are present in the text or speech, helping the software to understand the key information. Named entities would be divided into categories, such as people’s names, business names and geographical locations. Numeric entities would be divided into number-based categories, such as quantities, dates, times, percentages and currencies.

• Natural language understanding aims to achieve human-like communication with computers by creating a digital system that can recognize and respond appropriately to human speech.
• If we were to explain it in layman’s terms or a rather basic way, NLU is where a natural language input is taken, such as a sentence or paragraph, and then processed to produce an intelligent output.
• It can use many different methods to accomplish this, from tokenization, lemmatization, machine translation and natural language understanding.
• Whether you’re on your computer all day or visiting a company page seeking support via a chatbot, it’s likely you’ve interacted with a form of natural language understanding.
• Help your business get on the right track to analyze and infuse your data at scale for AI.
• Natural Language Understanding (NLU) refers to the process by which machines are able to analyze, interpret, and generate human language.

This allows marketers to target their campaigns more precisely and make sure their messages get to the right people. 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. 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. For instance, the word “bank” could mean a financial institution or the side of a river.

Answering customer calls and directing them to the correct department or person is an everyday use case for NLUs. Implementing an IVR system allows businesses to handle customer queries 24/7 without hiring additional staff or paying for overtime hours. Automated reasoning is a subfield of cognitive science that is used to automatically prove mathematical theorems or make logical inferences about a medical diagnosis. It gives machines a form of reasoning or logic, and allows them to infer new facts by deduction. Ideally, your NLU solution should be able to create a highly developed interdependent network of data and responses, allowing insights to automatically trigger actions.