What Is Natural Language Understanding NLU ?

Natural Language Understanding and Natural Language Processes have one large difference. NLP is an umbrella term that encompasses any and everything related to making machines able to process natural language, whether it’s receiving the input, understanding the input, or generating a response. NLU technology can also help customer support agents gather information from customers and create personalized responses.

Accenture reports that 91% of consumers say they are more likely to shop with companies that provide offers and recommendations that are relevant to them specifically. Using our example, an unsophisticated software tool could respond by showing data for all types of transport, and display timetable information rather than links for purchasing tickets. Without being able to infer intent accurately, the user won’t get the response they’re looking for. Intent recognition identifies what the person speaking or writing intends to do. Identifying their objective helps the software to understand what the goal of the interaction is.

What is Natural Language Processing?

Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. 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. Instead they are different parts of the same process of natural language elaboration. More precisely, it is a subset of the understanding and comprehension part of natural language processing.

Automate data capture to improve lead qualification, support escalations, and find new business opportunities. For example, ask customers questions and capture their answers using Access Service Requests (ASRs) to fill out forms and qualify leads. 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.

How does Natural Language Understanding Work?

Natural Language Understanding enables machines to understand a set of text by working to understand the language of the text. There are so many possible use-cases for NLU and NLP and as more advancements are made in this space, we will begin to see an increase of uses across all spaces. IVR, or Interactive Voice Response, is a technology that lets inbound callers use pre-recorded messaging and options as well as routing strategies to send calls to a live operator. Using NLU, voice assistants can recognize spoken instructions and take action based on those instructions. For example, a user might say, “Hey Siri, schedule a meeting for 2 pm with John Smith.” The voice assistant would use NLU to understand the command and then access the user’s calendar to schedule the meeting. Similarly, a user could say, “Alexa, send an email to my boss.” Alexa would use NLU to understand the request and then compose and send the email on the user’s behalf.

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. Natural language understanding (NLU) is an artificial intelligence-powered technology that allows machines to understand human language. The technology sorts through mispronunciations, lousy grammar, misspelled words, and sentences to determine a person’s actual intent. To do this, NLU has to analyze words, syntax, and the context and intent behind the words.

For example, allow customers to dial into a knowledge base and get the answers they need. Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions. Data pre-processing aims to divide the natural language content into smaller, simpler sections.

This allows us to resolve tasks such as content analysis, topic modeling, machine translation, and question answering at volumes that would be impossible to achieve using human effort alone. Natural language understanding (NLU) is a branch of natural language processing that deals with extracting meaning from text and speech. To do this, NLU uses semantic and syntactic analysis to determine the intended purpose of a sentence. Semantics alludes to a sentence’s intended meaning, while syntax refers to its grammatical structure. NLU is an evolving and changing field, and its considered one of the hard problems of AI. Various techniques and tools are being developed to give machines an understanding of human language.

With text analysis solutions like MonkeyLearn, machines can understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket. Not only does this save customer support teams hundreds of hours, but it also helps them prioritize urgent tickets. Intent recognition is another aspect in which NLU technology is widely used. It involves understanding the intent behind a user’s input, whether it be a query or a request. NLU-powered chatbots and virtual assistants can accurately recognize user intent and respond accordingly, providing a more seamless customer experience. Another important application of NLU is in driving intelligent actions through understanding natural language.

Millions of businesses already use NLU-based technology to analyze human input and gather actionable insights. NLU tools should be able to tag and categorize the text they encounter appropriately. Without sophisticated software, understanding implicit factors is difficult. In addition to making chatbots more conversational, AI and NLU are being used to help support reps do their jobs better. Additionally, NLU establishes a data structure specifying relationships between phrases and words.

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. The tokens are run through a dictionary that can identify a word and its part of speech. The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning. A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand.

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. If users deviate from the computer’s prescribed way of doing things, it can cause an error message, a wrong response, or even inaction. However, solutions like the Expert.ai Platform have language disambiguation capabilities to extract meaningful insight from unstructured language data. Through a multi-level text analysis of the data’s lexical, grammatical, syntactical, and semantic meanings, the machine will provide a human-like understanding of the text and information that’s the most useful to you. In other words, NLU is Artificial Intelligence that uses computer software to interpret text and any type of unstructured data. NLU can digest a text, translate it into computer language and produce an output in a language that humans can understand.

NLP Vs. NLU Vs. NLG: What’s The Difference?

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. Natural language understanding can nlu definition help speed up the document review process while ensuring accuracy. With NLU, you can extract essential information from any document quickly and easily, giving you the data you need to make fast business decisions. Manual ticketing is a tedious, inefficient process that often leads to delays, frustration, and miscommunication.

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. John Ball, cognitive scientist and inventor of Patom Theory, supports this assessment. Natural language processing has made inroads for applications to support human productivity in service and ecommerce, but this has largely been made possible by narrowing the scope of the application.

• Thus, it helps businesses to understand customer needs and offer them personalized products.
• Without sophisticated software, understanding implicit factors is difficult.
• 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.
• Supervised models based on grammar rules are typically used to carry out NER tasks.

Machine learning is at the core of natural language understanding (NLU) systems. It allows computers to “learn” from large data sets and improve their performance over time. Machine learning algorithms use statistical methods to process data, recognize patterns, and make predictions.

• Natural language understanding is considered a problem of artificial intelligence.
• Part of this caring is–in addition to providing great customer service and meeting expectations–personalizing the experience for each individual.
• Customer support becomes more efficient with intelligent chatbots capable of empathetic responses, while interactive voice response (IVR) systems offer seamless interactions, leading to enhanced customer experiences.
• By allowing machines to comprehend human language, NLU enables chatbots and virtual assistants to interact with customers more naturally, providing a seamless and satisfying experience.