What is Natural Language Understanding & How Does it Work?

The question “what’s the weather like outside?” can be asked in hundreds of ways. With NLU, computer applications can recognize the many variations in which humans say the same things. The combination of NLP and NLU has revolutionized various applications, such as chatbots, voice assistants, sentiment analysis systems, and automated language translation. Chatbots powered by NLP and NLU can understand user intents, respond contextually, and provide personalized assistance. Similarly, NLU is expected to benefit from advances in deep learning and neural networks.

NLU is not just a technological advancement; it’s a bridge that connects the vast realm of human communication with the limitless potential of artificial intelligence. With Akkio’s intuitive interface and built-in training models, even beginners can create powerful AI solutions. Beyond NLU, Akkio is used for data science tasks like lead scoring, fraud detection, churn prediction, or even informing healthcare decisions. 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

Without using NLU tools in your business, you’re limiting the customer experience you can provide. Two key concepts in natural language processing are intent recognition and entity recognition. NLU enables computers to understand the sentiments expressed in a natural language used by humans, such as English, French or Mandarin, without the formalized syntax of computer languages. NLU also enables computers to communicate back to humans in their own languages. 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.

NLP (i.e. NLU and NLG) on the other hand, can provide an understanding of what the customers “say”. Without NLP, a chatbot cannot meaningfully differentiate between responses like “Hello” and “Goodbye”. Natural Language Generation(NLG) is a sub-component of Natural language processing that helps in generating the output in a natural language based on the input provided by the user. This component responds to the user in the same language in which the input was provided say the user asks something in English then the system will return the output in English.

Understanding Chatbot AI: NLP vs. NLU vs. NLG

In NLU systems, natural language input is typically in the form of either typed or spoken language. Text input can be entered into dialogue boxes, chat windows, and search engines. Similarly, spoken language can be processed by devices such as smartphones, home assistants, and voice-controlled televisions. NLU algorithms analyze this input to generate an internal representation, typically in the form of a semantic representation or intent-based models. Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between machines and human (natural) languages. As its name suggests, natural language processing deals with the process of getting computers to understand human language and respond in a way that is natural for humans.

Akkio also offers integrations with a wide range of dataset formats and sources, such as Salesforce, Hubspot, and Big Query. 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.

Recommendations on Spotify or Netflix, auto-correct and auto-reply, virtual assistants, and automatic email categorization, to name just a few. Both NLP and NLU aim to make sense of unstructured data, but there is a difference between the two. 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. Such tasks can be automated by an NLP-driven hospitality chatbot (see Figure 7).

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. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently. Help your business get on the right track to analyze and infuse your data at scale for AI. Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time. Accurately translating text or speech from one language to another is one of the toughest challenges of natural language processing and natural language understanding.

NLU proceeds with syntax and grammar analysis after dissecting the text into tokens. Advanced parsing techniques are employed to construct a syntactic tree that represents the grammatical structure of the text, allowing NLU systems to navigate the intricacies of language structure. 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. Rule-based systems use a set of predefined rules to interpret and process natural language. These rules can be hand-crafted by linguists and domain experts, or they can be generated automatically by algorithms.

What is NLP?

According to Zendesk, tech companies receive more than 2,600 customer support inquiries per month. 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.

For instance, a simple chatbot can be developed using NLP without the need for NLU. However, for a more intelligent and contextually-aware assistant capable of sophisticated, natural-sounding conversations, natural language understanding becomes essential. It enables the assistant to grasp the intent behind each user utterance, ensuring proper understanding and appropriate responses. The models examine context, previous messages, and user intent to provide logical, contextually relevant replies. It also facilitates sentiment analysis, which involves determining the sentiment or emotion expressed in a piece of text, and information retrieval, where machines retrieve relevant information based on user queries.

When Sian is not busily leading SAS Press, she is a devoted soccer/baseball mom to her two boys and walking Chuck, the family chocolate lab. Here the user intention is playing cricket but however, there are many possibilities that should be taken into account. Difference between NLP, NLU, NLG and the possible things which can be achieved when implementing an NLP engine for chatbots.

This will empower your journey with confidence that you are using both terms in the correct context. The goal of a chatbot is to minimize the amount of time people need to spend interacting with computers and maximize the amount of time they spend doing other things. Answering customer calls and directing them to the correct department or person is an everyday use case for NLUs.

One of the primary goals of NLP is to bridge the gap between human communication and computer understanding. By analyzing the structure and meaning of language, NLP aims to teach machines to process and interpret natural language in a way that captures its nuances and complexities. NLG techniques provide ideas on how to build symbiotic systems that can take advantage of the knowledge and capabilities of both humans and machines. While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities.

A sophisticated NLU solution should be able to rely on a comprehensive bank of data and analysis to help it recognize entities and the relationships between them. 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. This is just one example of how natural language processing can be used to improve your business and save you money.

Thus, it helps businesses to understand customer needs and offer them personalized products. Data pre-processing aims to divide the natural language content into smaller, simpler sections. 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.

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. While the road ahead is filled with challenges, from privacy concerns to real-time processing and the dynamic nature of language, the NLU community is committed to advancing the field. In this ongoing journey, NLU remains a cornerstone in the bridge between humans and machines, transforming how we communicate, collaborate, and connect in an increasingly digital world.

• Forethought’s own customer support AI uses NLU as part of its comprehension process before categorizing tickets, as well as suggesting answers to customer concerns.
• Using symbolic AI, everything is visible, understandable and explained within a transparent box that delivers complete insight into how the logic was derived.
• ”, NLU is able to recognize that the user is asking for a particular type of information and can then provide an appropriate response.
• These technologies allow chatbots to understand and respond to human language in an accurate and natural way.
• Just like learning to read where you first learn the alphabet, then sounds, and eventually words, the transcription of speech has evolved over time with technology.
• Natural Language Generation (NLG) is an essential component of Natural Language Processing (NLP) that complements the capabilities of natural language understanding.

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. Easily import Alexa, DialogFlow, or Jovo NLU models into your software on all Spokestack Open Source platforms. Integrate a voice interface into your software by responding to an NLU intent the same way you respond to a screen tap or mouse click.

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