What is Natural language understanding (NLU)?

Natural language understanding (NLU) is a part of artificial intelligence (AI) focused on teaching computers how to understand and interpret human language as we use it naturally.

How does natural language understanding work?

NLU processes typically involve the following stages:

  • Preprocessing: Text is broken down into smaller units like words or phrases. (tokenization) and might be normalized (e.g., converting slang to standard words)
  • Semantic analysis: This includes analysis of individual words and their relationships within a sentence.
  • Syntactic analysis: Identifies the grammatical structure of sentences – parts of speech, dependencies between words.
  • Contextual understanding: NLU relies on its memory and the bigger conversation picture to clear up confusion and determine what someone wants.

Natural language understanding applications

  • Chatbots and virtual assistants
  • Search engines
  • Sentiment analysis
  • Machine translation

NLU vs. NLP vs. NLG

The main difference between NLP, NLU, and NLG is their focus.

NLP (natural language processing) is concerned with all aspects of computer processing of human language. At the same time, NLU focuses on understanding the meaning of human language, and NLG (natural language generation) focuses on generating human language from computer data.

In other words:

NLP is about understanding and processing human language.
NLU is about understanding human language.
NLG is about generating human language.

Importance of natural language understanding

Simplifies communication between computers and people: NLU is the key to making computers and systems genuinely interact with us the way we communicate with each other.

This leads to:

  • Intuitive interfaces in chatbots and virtual assistants
  • More useful voice-controlled devices and smart speakers

Data understanding: Vast amounts of text data (like social media posts, surveys, and emails) are hard for traditional analysis tools to decipher. NLU allows machines to understand meaning in this data, extracting sentiment, trends, and customer insights.

Improved search: Instead of relying on keywords, NLU-powered search engines can grasp intent. This means you get more relevant results even when you don’t phrase your query perfectly.

Streamlining processes: Many businesses have tasks involving document analysis, classification, and information extraction.

NLU helps:

  • Automate these processes
  • Saves time and resources

Specific areas where NLU makes a difference

Customer service

NLU-powered chatbots provide 24/7 support, resolving common issues quickly.

Sentiment analysis of customer feedback identifies problems and improvement areas.

Market research

Social media analysis with NLU reveals trends and customer attitudes toward brands and products.

NLU extracts information from market reports and financial news for faster analysis.


Assists in analyzing medical records and extracting relevant information.

NLU-powered symptom checkers can provide initial guidance to patients.


More sophisticated search functions on product websites for easier discovery.

Chatbots offer personalized product recommendations.

Natural language understanding examples

Customer service

Chatbots: NLU powers chatbots to understand customer questions and requests, provide relevant answers, and even route inquiries to the appropriate human agent.

Automated ticket routing: NLU systems can analyze the content of customer support tickets to determine the nature of the issue and automatically route them to the correct department or team specializing in solving that problem.

Sentiment analysis: NLU can identify the overall emotion and attitude behind customer feedback in emails, surveys, and social media posts. This helps businesses track customer satisfaction and pinpoint areas needing attention.

Search engines

Understanding search intent: Instead of matching keywords, search engines with NLU capabilities understand the intent behind a user’s query. This means more relevant results, even when the search phrasing isn’t exact.

Autocomplete: As users start typing, NLU-powered search engines can predict what they are likely seeking, saving time and effort.


Product search: NLU makes product searches on eCommerce sites more intuitive. Shoppers can use natural language like “show me women’s boots for winter” instead of a specific keyword combination.

Personalized recommendations: Chatbots and recommendation systems leverage NLU to analyze a customer’s purchases, browsing history, and expressed preferences to offer tailored product suggestions.

Social media monitoring

Brand sentiment analysis: Businesses can employ NLU tools to analyze vast numbers of social media conversations, extract sentiment towards their brand, track trends, or gauge reactions to new products or campaigns.

Automated response suggestions: NLU can suggest appropriate responses for social media teams when interacting with customers. This speeds up the process while ensuring responses are on-brand and helpful.

Other examples

Voice assistants (Siri, Alexa): NLU allows these devices to understand commands and questions in conversational language.

Machine translation: NLU helps improve translation accuracy, moving beyond word-for-word substitution to capture the intent and nuances of the original text.

Content analysis: NLU assists with tasks like summarizing lengthy documents, extracting key facts, and classifying text based on topic or tone.


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