What Is Natural Language Understanding?

Businesses extract several benefits from natural language understanding technology.

Increase customer engagement

Natural language understanding is the technology behind automated conversational experiences in contact centers, social media platforms, and mobile applications. You can quickly automate customer service interactions, decrease inquiry response times, and meet customer service expectations at reduced cost.

For example, when customers call a business number, they can interact with an AI chat interface that understands their query and directs them to the right human agent. Interactive voice response (IVR) is another technology powered by natural language understanding that allows customers to use their voice to navigate call menus. It streamlines the initial stages of customer support, saves time, and improves customer engagement.

Enable intelligent search

Natural language understanding plays a key role in intelligent search technology. Intelligent search can understand natural language questions from end users and search through forums and internal knowledge databases to find the specific information needed. Due to NLU, it can summarize and present information from multiple sources in concise paragraphs.

Optimize business operations

Natural language understanding can help automate repetitive tasks, reducing effort and expediting service delivery. For example, you can create flows for common tasks like making payments or sending transaction progress updates. By collecting information upfront, you can also achieve faster troubleshooting. Employees can then use their time on higher-value activities and improve productivity.

Personalize user experiences

Natural language understanding technologies can transcribe user input, identify user intent, and deliver personalized experiences. By integrating NLU with consumer data and behavior patterns, your business can provide unique offers or tailored messages to your customers.

Natural language understanding takes text as input and interprets it similarly to humans. It works in three main stages.

Tokenization

Tokenization breaks down natural language input into smaller language units, like specific words or unique sounds. It also handles contractions and punctuation. For example, the sentence “I’m going to John’s house!” might be tokenized as:

[“I”, “’m”, “going”, “to”, “John”, “’s”, “house”, “!”] This step helps NLU systems identify sentence and paragraph components.

Lexical analysis

Lexical analysis helps to give specific words their context. It analyzes each token to determine its meaning based on dictionaries, parts of speech, and context. This step helps distinguish between words that have multiple meanings. For example, in the sentence “I left my watch on the table,” the word “watch” could mean:

• A timepiece (noun)

• The act of observing something (verb)

Lexical analysis, aided by context, determines that “watch” above refers to a physical object rather than an action. Another example is handling synonyms. In the sentence “She purchased a new laptop,” lexical analysis recognizes that “purchased” is synonymous with “bought.”

Syntactic analysis

Syntactic analysis determines the overall sentence structure, looking at its syntax to determine how words fit together. It ensures that the sentence follows the syntax rules and identifies components like subject, verb, and object. For example, in the sentence “The quick brown fox jumps over the lazy dog,” a syntactic parser breaks it down into:

• Subject: “The quick brown fox”

• Verb: “jumps”

• Prepositional phrase: “over the lazy dog.”

Syntax and sentence structure also help natural language understanding software understand user intent. Consider the difference between:

• “Can you book a flight to Paris?” (A request to book a flight)

• “Can you tell me if I can book a flight to Paris?” (A request for information about booking)

Syntactic analysis helps NLU tools understand more complex queries and deliver the intended function.

Modern NLU technology should have the following capabilities.

Generative AI

Generative artificial intelligence refers to large language models trained on vast amounts of data and capable of language processing in ways impossible with traditional NLU technology. It helps to improve user experience and offers a range of new features. For example, NLU with generative AI can:

• Provide automated responses to frequently asked questions.

• Analyze customer sentiment and intents to route calls appropriately.

• Generate conversation summaries to help agents.

• Automatically generate emails or chat responses to common workflows.

This new generation of AI-powered assistants provide seamless self-service experiences that delight customers. NLU technology that uses generative artificial intelligence can write emails based on vocal commands or rapidly summarize conversations.

High-quality speech recognition

Users may have distinct accents or ways of speaking. This speech diversity should not be a restriction for NLU tools. You want an NLU system that learns how users can express their intent based on a few sample utterances. It should understand human speech and deliver the intended response for all demographics.

Context management for multi-turn dialog

Effective NLU tools must store and track temporary contextual information throughout a conversation, understand user inputs in a way that mimics human-to-human interactions, and maintain coherence across multiple exchanges, ensuring a smooth conversational experience.

For example, if a user initiates a hotel booking request with a voice chatbot by saying, “I need a hotel in New York,” the chatbot should:

• Recognize that additional details are needed and respond with relevant follow-up questions such as “For how many nights?” and “What is your check-in date?”

• Retain previous user inputs so that when the user specifies, “Three nights, checking in on March 10,” it correctly associates this information with the New York hotel request.

• Handle pronouns so that if the user continues with “Actually, make it four nights,” the chatbot should understand that “it” refers to the hotel booking.

• Handle implicit references and topic shifts. For example, in a customer service chatbot, if a user asks, “Where’s my order?” and later says, “Can I change the delivery address?” the system should recognize that “the delivery address” refers to the same order previously mentioned.

Ease of implementation

Natural language understanding tools should be easy to build and set up with little technical support. Even non-technical users should be able to set up conversational workflows with NLU software. Look for visual drag-and-drop conversation builders that are easy to design and test.