What is NLU Natural Language Understanding?

In our Conversational AI Cloud, we introduced generative AI for generating conversational content and completely overhauled the way we do intent classification, further improving Conversational AI Cloud’s multi-engine NLU. Meanwhile, our teams have been working hard to introduce conversation summaries in CM.com’s Mobile Service Cloud. 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.

Chatbots are necessary for customers who want to avoid long wait times on the phone. With NLU (Natural Language Understanding), chatbots can become more conversational and evolve from basic commands and keyword recognition. With the advent of voice-controlled technologies like Google Home, consumers are now accustomed to getting unique replies to their individual queries; for example, one-fifth of all Google searches are voice-based. You’re falling behind if you’re not using NLU tools in your business’s customer experience initiatives. With today’s mountains of unstructured data generated daily, it is essential to utilize NLU-enabled technology.

The difference between NLU, NLP, and NLG

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 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.

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. NLP processes flow through feedback loop with machine learning to improve the computer’s artificial intelligence algorithms.

When are machines intelligent?

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.

NLP and NLU are similar but differ in the complexity of the tasks they can perform. NLP focuses on processing and analyzing text data, such as language translation or speech recognition. NLU goes a step further by understanding the context and meaning behind the text data, allowing for more advanced applications such as chatbots or virtual assistants. Natural language output, on the other hand, is the process by which the machine presents information or communicates with the user in a natural language format.

These rules can be hand-crafted by linguists and domain experts, or they can be generated automatically by algorithms. For example, after training, the machine can identify “help me recommend a nearby restaurant”, which is not an expression of the intention of “booking a ticket”. In the context of a conversational AI platform, if a user were to input the phrase ‘I want to buy an iPhone,’ the system would understand that they intend to make a purchase and that the entity they wish to purchase is an iPhone. This allows the system to provide a structured, relevant response based on the intents and entities provided in the query.

Beyond merely investing in AI and machine learning, leaders must know how to use these technologies to deliver value. Natural language understanding is an artificial intelligence technology who’s main job is understanding spoken or written words and phrases. 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. The task of NLG is to generate natural language from a machine-representation system such as a knowledge base or a logical form. To simplify this, NLG is like a translator that converts data into a “natural language representation”, that a human can understand easily.

Therefore your NLU might recognise that phrase as a ‘booking’ phrase and initiate your booking intent. Training data, also called ‘sample utterances’ are simply written examples of the kind of things people are likely to say to a chatbot or voicebot.

The difference between natural language understanding and natural language generation is that the former deals with a computer’s ability to read comprehension, while the latter pertains to a machine’s writing capability. Now, businesses can easily integrate AI into their operations with Akkio’s no-code AI for NLU. With Akkio, you can effortlessly build models capable of understanding English and any other language, by learning the ontology of the language and its syntax. Even speech recognition models can be built by simply converting audio files into text and training the AI. It’s often used in conversational interfaces, such as chatbots, virtual assistants, and customer service platforms. NLU can be used to automate tasks and improve customer service, as well as to gain insights from customer conversations.

NLU vs. NLP: What is the connection?

Overall, NLU technology is set to revolutionize the way businesses handle text data and provide a more personalized and efficient customer experience. 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.

It gives machines a form of reasoning or logic, and allows them to infer new facts by deduction. Both NLP and NLU aim to make sense of unstructured data, but there is a difference between the two. Read more about our conversation intelligence platform or chat with one of our experts.

What is natural language processing?

When NLP breaks down a sentence, the NLU algorithms come into play to decipher its meaning. It is quite possible that the same text has various meanings, or different words have the same meaning, or that the meaning changes with the context. But don’t confuse them yet, it is correct that all three of them deal with human language, but each one is involved at different points in the process and for different reasons. With an agent AI assistant, customer interactions are improved because agents have quick access to a docket of all past tickets and notes. This data-driven approach provides the information they need quickly, so they can quickly resolve issues – instead of searching multiple channels for answers.

For example, programming languages including C, Java, Python, and many more were created for a specific reason. Where NLP helps machines read and process text and NLU helps them understand text, NLG or Natural Language Generation helps machines write text. This is exactly why instant-messaging apps have become so natural for both personal and professional communication.

With Akkio, you can develop NLU models and deploy them into production for real-time predictions. A lot of acronyms get tossed around when discussing artificial intelligence, and NLU is no exception. NLU, a subset of AI, is an umbrella term that covers NLP and natural language generation (NLG). Natural language understanding means that the machine is like a human being, and has the ability to understand the language of a normal person. Because natural language has many difficulties in understanding (detailed below), NLU is still far from human performance. The “suggested text” feature used in some email programs is an example of NLG, but the most well-known example today is ChatGPT, the generative AI model based on OpenAI’s GPT models, a type of large language model (LLM).

• Individuals working in NLP may have a background in computer science, linguistics, or a related field.
• This enables other computer systems to process the data to fulfil user requests.
• Natural language understanding is an artificial intelligence technology who’s main job is understanding spoken or written words and phrases.
• NLU helps to improve the quality of clinical care by improving decision support systems and the measurement of patient outcomes.

An RNN can be trained to recognize different objects in an image or to identify the various parts of speech in a sentence. If you’re interested in learning more about what goes into making AI for customer support possible, be sure to check out this blog on how machine learning can help you build a powerful knowledge base. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. IBM has innovated in the AI space by pioneering NLP-driven tools and services that enable organizations to automate their complex business processes while gaining essential business insights. These syntactic analytic techniques apply grammatical rules to groups of words and attempt to use these rules to derive meaning. ATNs and their more general format called „generalized ATNs“ continued to be used for a number of years.

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