What Is Natural Language Understanding (NLU) & How Does It Work?

Artificial intelligence is making breakthroughs in understanding human communication. From detecting emotions using facial recognition to automatically sorting and generating complex texts. 

For businesses, the benefits of AI are huge. Customers express their opinions all the time – on social media, in online reviews, survey responses, etc. – creating troves of data that can be evaluated and put to use. 

With the help of natural language understanding (NLU) and machine learning software, computers can automatically analyze data in seconds, saving businesses countless hours and resources. In this post, we’ll go over what natural language understanding is, how it helps businesses, and how to get started with NLU.

What Is Natural Language Understanding?

Natural language understanding (NLU) is a sub-topic of natural language processing, which involves breaking down the human language into a machine-readable format. Interesting applications include text categorization, machine translation, and question answering.  

NLU uses grammatical rules and common syntax to understand the overall context and meaning of “natural language,” beyond literal definitions. Its goal is to understand written or spoken language the way a human would.

NLU is used in natural language processing (NLP) tasks like topic classification, language detection, and sentiment analysis:

  • Sentiment analysis automatically interprets emotions within a text and categorizes them as positive, negative, or neutral. By quickly understanding, processing, and analyzing thousands of online conversations, sentiment analysis tools can deliver valuable insights about how customers view your brand and products.

Happy, neutral, and sad emojis to explain how sentiment analysis groups text by positive, neutral, and negative.

  • Language detection automatically understands the language of written text. An essential tool to help businesses route tickets to the correct local teams,  avoid wasting time passing tickets from one customer agent to the next, and respond to customer issues faster.
  • Topic classification is able to understand natural language to automatically sort texts into predefined groups or topics. Software company Atlassian, for example, uses the tags Reliability, Usability, and Functionality to sort incoming customer support tickets, enabling them to deal with customer issues efficiently.

NLP Vs NLU: What’s The Difference?

Natural language understanding is a subfield of natural language processing

While both NLP and NLU aim to make sense of unstructured data, but they are not the same thing.

NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans. 

Natural language understanding, on the other hand, focuses on a machine’s ability to understand the human language. NLU refers to how unstructured data is rearranged so that machines may “understand” and analyze it.

Look at it this way. Before a computer can process unstructured text into a machine-readable format, first machines need to understand the peculiarities of the human language.

Natural Language Understanding Examples

Machine Translation (MT)

Accurately translating text or speech from one language to another is one of the toughest challenges of natural language processing and natural language understanding. 

Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation.

You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools. Google Translate even includes optical character recognition (OCR) software, which allows machines to extract text from images, read and translate it.

Automated Reasoning

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.

Simply put, using previously gathered and analyzed information, computer programs are able to generate conclusions. For example, in medicine, machines can infer a diagnosis based on previous diagnoses using IF-THEN deduction rules.

Automatic Routing of Tickets

A useful business example of  NLU is customer service automation. 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.

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.

Try out MonkeyLearn’s pre-trained machine learning models automatically tag your customer service tickets, or train your own for more accurate results.

Question Answering

Question answering is a subfield of NLP and speech recognition that uses NLU to help computers automatically understand natural language questions.

For example, here’s a common question you might ask Google Assitant:  “What’s the weather like tomorrow?”

NLP tools can split this question into topic (weather) and date (tomorrow), understand it and gather the most appropriate answer from unstructured collections of “natural language documents”: online news reports, collected web pages, reference texts, etc

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.

Get Started with Natural Language Understanding in AI

The above are only a handful of NLU examples and applications. AI technology has become fundamental in business, whether you realize it or not. Recommendations on Spotify or Netflix, auto-correct and auto-reply, virtual assistants, and automatic email categorization, to name just a few. 

Learn how to extract and classify text from unstructured data with  MonkeyLearn’s no-code, low-code text analysis tools. With natural language processing and machine learning working behind the scenes, all you need to focus on is using the tools and helping them to improve their natural language understanding. 

Request a demo and begin your natural language understanding journey in AI.

Rachel Wolff

Rachel Wolff

BA in journalism and French from Sheffield University. Interested in human-machine collaboration and Google's ever-changing algorithms.

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