- What Is Natural Language Understanding?
- What’s the Difference Between NLU and NLP?
- Natural Language Understanding: Applications and Examples
- Get Started with Natural Language Understanding
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, online reviews, surveys, etc. – creating troves of data that can be evaluated and put to use.
There are 2.5 quintillion bytes of data created every day. It’s hard to even wrap your head around what that means, and completely impossible for a team of human analysts to process all that data manually.
With the help of NLU, computers can automatically analyze that data in seconds, saving businesses countless hours and resources.
What Is Natural Language Understanding?
Natural language understanding (NLU) is the ability of machines to understand human language. By breaking down human language into segments, machines are able to comprehend online comments, perform language translation, detect emotions, and much more.
Among other linguistic components, 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 can be used for natural language processing (NLP) tasks like topic classification, language detection, and sentiment analysis – just a few of the text analysis tasks we perform here at MonkeyLearn.
Our sentiment analysis model, for example, can automatically interpret emotions within text as positive, negative, or neutral. By quickly “reading” through thousands of online conversations, the model will deliver insights about how customers feel about your brand and products.
Language detection, on the other hand, automatically detects the language of any written text. If you work for a global company, using language detection tools is essential to route tickets to the correct local teams. This means your teams don’t waste time passing tickets from one customer agent to the next, and they can respond to customer issues faster.
Finally, topic classification is a machine learning technique used to automatically read through texts and tag them with topics you may be interested in. Software company Atlassian, for example, decided to tag their incoming customer support tickets by Reliability, Usability, and Functionality, enabling them to deal with customer issues efficiently and gain accurate insights.
What’s the Difference Between NLU and NLP?
Natural language understanding is a subfield of natural language processing.
NLP is the umbrella term for all of the systems used to facilitate “natural” back-and-forth communication between computers and humans, in human language.
Natural language understanding, on the other hand, concerns only the machine’s ability to comprehend human language. NLU refers to how unstructured data is rearranged so that machines may “understand” and analyze it.
Humans can easily comprehend misspellings, misused words, incorrect grammar, sarcasm, abbreviations, etc. NLU uses artificial intelligence to help machines better understand these peculiarities.
Natural Language Understanding: Applications and Examples
The ability to accurately translate 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 App have become some of the leaders in the field of “generic” language translation.
On Google Translate you can type text or upload whole documents and receive translations in dozens of languages. Even more impressive is their app that allows you to take photos of menus, books, signs, etc. The text is then detected by optical character recognition (OCR) software, so that machines can read, understand, and translate it.
Other companies, like Lingua Custodia, create machine translation programs designed for specific industries and use unique types of documents and individual domains that allow for superior translation.
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
One use of NLU that every customer-facing business professional can understand the need for is customer service automation. With the use of AI, firms can reduce manual customer support tasks and save hundreds of work hours.
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.
Question answering is a subfield of NLP and speech recognition that uses NLU to help computers automatically respond to questions posed by humans.
An example would be asking a virtual assistant (like Siri or Amazon Alexa), “What’s the weather like tomorrow?”
The computer program would then use NLU to break your question down into topic (weather) and date (tomorrow).
Once the question has been determined, the answers are gathered from structured databases. Or, in the above case, the answer would be pulled 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, which would be tagged as location.
The goal of question answering is to give the user a response in their natural language, rather than a list of or link to the relevant documents.
Get Started with Natural Language Understanding
The above are only a handful of NLU examples and uses. The technology has become so fundamental that you probably encounter it every day: web search queries, chatbots, email spam recognition.
You can learn how to extract and classify text from documents and web pages with MonkeyLearn’s text analysis models. It’s free and easy, and will give you first-hand experience with natural language understanding.
Create a free MonkeyLearn account and start using NLU to understand your data.
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