NLP vs NLU and the growing ability of machines to understand

In recent years, researchers have shown that adding parameters to neural networks improves their performance on language tasks. However, the fundamental problem of understanding language—the iceberg lying under words and sentences—remains unsolved. 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”.

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(2) The work can be carried out largely automatically, by having the agent learn about both language and the world through its own operation, bootstrapped by a high-quality core lexicon and ontology that is acquired by people. In Linguistics for the Age of AI, McShane and Nirenburg argue that replicating the brain would not serve the explainability goal of AI. “[Agents] operating in human-agent teams need to understand inputs to the degree required to determine which goals, plans, and actions they should pursue as a result of NLU,” they write.

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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. Question answering is a subfield of NLP and speech recognition nlu training data that uses NLU to help computers automatically understand natural language questions. Text analysis solutions enable machines to automatically understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket.

Deep learning models make extensive use of automation, ingesting and using unstructured data — such as text and images — to build comprehensive decision-making abilities. 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. This enables machines to produce more accurate and appropriate responses during interactions. NLG is a software process that turns structured data – converted by NLU and a (generally) non-linguistic representation of information – into a natural language output that humans can understand, usually in text format. 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.

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The potential for artificial intelligence to create labor-saving workarounds is near-endless, and, as such, AI has become a buzzword for those looking to increase efficiency in their work and automate elements of their jobs. Here is a benchmark article by SnipsAI, AI voice platform, comparing F1-scores, a measure of accuracy, of different conversational AI providers. It is best to compare the performances of different solutions by using objective metrics.

  • Matching word patterns, understanding synonyms, tracking grammar — these techniques all help reduce linguistic complexity to something a computer can process.
  • 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.
  • Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings.
  • In a machine learning context, the algorithm creates phrases and sentences by choosing words that are statistically likely to appear together.
  • By using NLU technology, businesses can automate their content analysis and intent recognition processes, saving time and resources.
  • In the world of AI, for a machine to be considered intelligent, it must pass the Turing Test.

This is exactly why instant-messaging apps have become so natural for both personal and professional communication. With the advent of ChatGPT, it feels like we’re venturing into a whole new world. 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, for our Conversational AI solution. Today has introduced a significant release for its Conversational AI Cloud and Mobile Service Cloud.

NLU & The Future of Language

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. Just think of all the online text you consume daily, social media, news, research, product websites, and more.

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A dialogue manager uses the output of the NLU and a conversational flow to determine the next step. With this output, we would choose the intent with the highest confidence which order burger. We would also have outputs for entities, which may contain their confidence score.

Natural Language Processing (NLP): 7 Key Techniques

NLU also enables the development of conversational agents and virtual assistants, which rely on natural language input to carry out simple tasks, answer common questions, and provide assistance to customers. One of the major applications of NLU in AI is in the analysis of unstructured text. With the increasing amount of data available in the digital world, NLU inference services can help businesses gain valuable insights from text data sources such as customer feedback, social media posts, and customer service tickets. 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.

Because NLU encapsulates processing of the text alongside understanding it, NLU is a discipline within NLP.. NLU enables human-computer interaction in the sense that as well as being able to convert the human input into a form the computer can understand, the computer is now able to understand the intent of the query. Once the intent is understood, NLU allows the computer to formulate a coherent response to the human input. There are various ways that people can express themselves, and sometimes this can vary from person to person.

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SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items. ML models can be categorized as supervised, unsupervised or semisupervised, which refers to the degree of human intervention and feedback used to train the algorithm. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. And also the intents and entity change based on the previous chats check out below. Chrissy Kidd is a writer and editor who makes sense of theories and new developments in technology. Formerly the managing editor of BMC Blogs, you can reach her on LinkedIn or at

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Voice assistants and virtual assistants have several common features, such as the ability to set reminders, play music, and provide news and weather updates. They also offer personalized recommendations based on user behavior and preferences, making them an essential part of the modern home and workplace. As NLU technology continues to advance, voice assistants and virtual assistants are likely to become even more capable and integrated into our daily lives. Furthermore, different languages have different grammatical structures, which could also pose challenges for NLU systems to interpret the content of the sentence correctly. Other common features of human language like idioms, humor, sarcasm, and multiple meanings of words, all contribute to the difficulties faced by NLU systems.

What Is The Difference Between NLU and NLP?

Using NLP, every inbound message and request can be reviewed and routed to the correct parties quickly with fewer errors. Humans want to speak to machines the same way they speak to each other — in natural language, not the language… In Figure 2, we see a more sophisticated manifestation of NLP, which gives language the structure needed to process different phrasings of what is functionally the same request. With a greater level of intelligence, NLP helps computers pick apart individual components of language and use them as variables to extract only relevant features from user utterances. Try out no-code text analysis tools like MonkeyLearn to  automatically tag your customer service tickets.

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