There’s a lot that goes into how artificial intelligence (AI) works — a few big components to AI include Natural Language Understanding (NLU), Natural Language Processing (NLP), and Machine Learning (ML).
Each of these subsets of AI have their own rules and work independently as a vital function of artificial intelligence.
To give you a better understanding of NLU, we’ve put together this guide to improve your working knowledge of what Natural Language Understanding is, what it does, how it works, and how it comes together with other AI properties to successfully impact customer support teams.
What is Natural Language Understanding (NLU)?
Natural language understanding is a type of artificial intelligence that understands sentences using text or speech.
NLU enables machines to understand human interaction. Computers can understand humans in different languages and communicate in their respective languages.
The purpose of NLU is to allow machines to interact with humans without supervision. More and more companies are leveraging NLU for improved customer service experience.
How Does Natural Language Understanding Work?
NLU uses algorithms to analyze data to form structured ontology, which are definitions and concepts for understanding relationships. NLU recognizes the entity and intent, then responds regardless of human error such misspelling or mispronunciation.
Intent recognition uses natural language processing and machine learning to understand text and speech. It is the first step in understanding the text meaning
Entity recognition is a type of NLU that identifies entities within text or speech, allowing machines to understand key information. NLU understands two types of entities: numeric and named. Numeric entities are categorized by numbers, currencies and percentages. Named entities are categorized by people, businesses and locations
What is Natural Language Processing?
Natural Language Processing is a branch of artificial intelligence that uses machine learning algorithms to help computers understand natural human language.
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.
What Is The Difference Between NLU and NLP?
Natural Language Understanding and Natural Language Processes have one large difference. While NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans, NLU is focused on a machine’s ability to understand that human language.
The aim is to process freeform natural language text, transforming it into a standard structure that an algorithm can then parse and understand.
Use Cases for Natural Language Understanding
We’re sure you’re aware, but NLU is being used all over the place.
Whether you’re on your computer all day or visiting a company page seeking support via a chatbot, it’s likely you’ve interacted with a form of natural language understanding. When it comes to customer support, companies utilize NLU in artificially intelligent chatbots and assistants, so that they can triage customer tickets as well as understand customer feedback. 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.
Here are a few more places NLU is making an impact.
IVR Message Routing
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. IVR uses speech recognition and NLU to understand the needs of a person.
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. IVR makes a great impact on customer support teams that utilize phone systems as a channel since it can assist in mitigating support needs for agents.
Data Capture
Another place utilizing NLU is data capture. Data capture is the process of extracting information from paper or electronic documents and converting it into data for key systems.
NLU is necessary in data capture since the data being captured needs to be processed and understood by an algorithm to produce the necessary results.
Machine Translation
Natural Language Understanding is also making things like Machine Translation possible. Machine Translation, also known as automated translation, is the process where a computer software performs language translation and translates text from one language to another without human involvement. The text is understood and processed through a series of algorithms.
Conversational Interfaces
Conversational interfaces, also known as chatbots, sit on the front end of a website in order for customers to interact with a business. Because conversational interfaces are designed to emulate “human-like” conversation, natural language understanding and natural language processing play a large part in making the systems capable of doing their jobs.
Customer Support and Service Through AI Personal Assistants
The last place that may come to mind that utilizes NLU is in customer service AI assistants. Not only is AI and NLU being used in chatbots that allow for better interactions with customers but AI and NLU are also being used in agent AI assistants that assist support representatives in doing their jobs better and more efficiently. Gone are the days of agents not knowing where to find certain information when a customer requests it, they can now compile all historical data and information in a docket that sits on a business’ helpdesk and is able to generate information based on past tickets and current notes.
NLU & The Future of Language
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.
If you’re interested in learning more about what goes into making AI for customer support possible, be sure to check out this blog on how machine learning can help you build a powerful knowledge base.
