NLU vs. NLP: What’s the Difference?

In the ever-expanding world of artificial intelligence (AI), it has become increasingly important for machines to understand and interact with humans, and two key technologies help. While sometimes used interchangeably, NLU and NLP have fundamental differences. However, these processes also make important technological partners. Explore NLU and NLP and learn about their uses, benefits, and challenges. Then, find out how and where you can learn more about these exciting technologies.

What is natural language understanding (NLU)?

NLU is a branch of study within AI and often a subset of NLP. It focuses on understanding text and speech by analyzing actual language instead of individual words or phrases. NLU allows machines to interact effectively with human users. Two key elements of NLU include:

1. Entity recognition: NLU identifies important data within language, including names of people, dates, and places. For instance, NLU can analyze “Sara and Jon met April 3 at the Washington Monument” and recognize that “Sara” and “Jon” are names, “April 3” is a date, and “Washington Monument” is a place.

2. Intent recognition: NLU understands the context or user intent within language, including performing a function or seeking information. For instance, NLU can understand that when a user states, “Reorder laundry soap,” the user wants the program to search their order history for the brand, size, and formula of laundry detergent that they purchase most often and add that same laundry detergent to their online shopping cart.

What is NLU used for?

NLU helps machines interact with humans effectively by recognizing and mimicking verbal and written human messages. This function plays an important role in a variety of applications, including:

• Call steering: NLU allows customers to explain the reason for a call rather than navigate various menus. For instance, NLU can allow an internet company’s callers to state “reset modem” to indicate that they want to speak to a representative who can help them reset their modem after a power outage.

• Chatbots: NLU allows chatbots to provide information or direct users to the correct department for quick and efficient customer service. For instance, NLU can allow the chatbot on a retailer’s website to list a brick-and-mortar store’s holiday hours or provide a link to the retailer’s return policy.

• Unstructured text analysis: NLU can help companies learn more about their customer base and target new customers by analyzing unstructured text, such as social media comments or customer feedback. For instance, NLU can enable a cosmetics company to analyze the comments under an Instagram post announcing its new foundation to discover that potential customers love the idea of the product but wish that it were available for a wider variety of skin tones.

• Virtual assistants: NLU helps virtual assistants understand and respond to human language, which allows them to perform tasks like scheduling appointments, providing information, and playing requested movies or songs. For instance, NLU can allow users to ask their device, “Is it going to snow today?” and the device can respond that the forecast reports an expected 4 inches of snow that day.

Benefits of NLU

NLU works alongside natural language generation (NLG), another subset of NLP, to help machines understand human language, respond with human-like language, and ultimately enhance interactions between machines and humans. 

Successful machine-human interaction offers several benefits to companies, including:

• Creation of better products through the integration of customer feedback

• Customer support and satisfaction enhancements

• Marketing technique improvements

• Multilingual translation and communication

• Time and money savings

Challenges of NLU

Although NLU has many benefits, it also has several challenges, including: 

• Lexical (or semantic) ambiguity: NLU has challenges with the potential for words or phrases to have multiple meanings. For instance, the word "bark" can mean the sound a dog makes or the outer surface of a tree. 

• Syntax (or structural) ambiguity: NLU may not be able to interpret a sentence accurately because it could have different meanings based on its structure. For instance, the sentence “Mr. Smith said he would give a test on Wednesday” could mean that the test would be on Wednesday or that Mr. Smith made the statement about a test on Wednesday but did not indicate what day the test would be. 

• Contextual elements: NLU may not be able to recognize the additional elements that add context to human language, like humor, irony, and sarcasm. For instance, NLU might not be able to recognize the sarcasm in the sentence, “Sam was thrilled that he had to wait for two hours at the Department of Motor Vehicles to renew his license.” 

What is natural language processing (NLP)?

NLP is a branch of study within the field of AI. It focuses on facilitating successful machine-human interaction by enabling machines to break down and understand human language and generate human-like language. NLP draws from a variety of fields, including:

• Cognitive science

• Computer science

• Linguistics

• Mathematics 

• Philosophy

• Statistics

What is the difference between natural language processing and neuro-linguistic programming?

Although they share the same acronym (NLP), natural language processing and neuro-linguistic programming differ in significant ways. While natural language processing is a technology that enables successful machine-human interaction, neuro-linguistic programming is a form of therapy that helps clients change the way they process information to address issues involving mental health or behavioral patterns. 

What is NLP used for?

NLP has varied uses across many sectors, including the automotive, banking, construction, education, customer service, and health care industries. Some of the common applications of NLP include:

• Content summarization: NLP can quickly summarize documents, records, and other content for faster and more informed decision-making. For instance, NLP can summarize patient records or medical research papers for doctors or other health care workers.

• Email filtering: NLP can analyze email messages’ contents to categorize them as primary, social, promotional, and spam. For instance, NLP can identify misleading words, repeated words or phrases, or unusual wording that often indicate that an email is spam.

• Speech recognition: NLP can transform spoken language into text by breaking it into manageable pieces called tokens. For instance, NLP can break apart the sentence “Frank is a Chihuahua” into the tokens “Frank,” “is,” “a,” and “Chihuahua.”

• Text extraction: NLP can help users extract specific pieces of information from large data sets, like keywords. For instance, NLP can extract the keywords from customer reviews of a company’s products or services that indicate customers’ happiness or displeasure with them, such as “friendly,” “sturdy,” and “well-made” or “rude,” “flimsy,” and “cheap.”

Benefits of NLP

One of the key benefits of NLP is that it enables humans and machines to interact, but NLP provides many other benefits to the people and companies that use it. Some of the common benefits of NLP are: 

• Better ability to analyze large volumes of data

• Increased document accuracy and efficiency

• Easy summarization of long-form reports and other text

• Improved products, services, and customer service 

Challenges of NLP

Even though NLP offers many important benefits, using it also presents some challenges, including:

• An inability to process data across many different languages

• Biases in the NLP models due to biases within the data used to train the models

• Difficulty in obtaining data sets large enough in size and high enough in quality to train the models effectively

NLU vs. NLP: Which should you choose?

Most companies use both NLU and NLP because NLU makes NLP function more accurately and efficiently. Simply put, NLU analyzes the meaning and context of language after NLP breaks it into chunks. Continuing advancements with NLU and NLP have made them an exciting and popular field to pursue for people interested in a career in AI.

Learn more about NLU and NLP on Coursera

Within the field of AI, NLU, and NLP work together to create machines that can respond more intelligently to humans. Learn the skills you’ll need to start working with NLU and NLP with Microsoft’s Natural Language Processing in Microsoft Azure, which is part of the Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization on Coursera.