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NLP vs NLU – Understand the Differences

By September 27, 2022April 26th, 2023Conversational Insights Glossary5 mins read


Ever wondered why you can talk to digital assistants like google assistant, Alexa, and Siri? NLP and NLU make it possible for computers to understand human speech.

There is a common misconception among many people: computers cannot understand human speech, which isn’t the case as artificial intelligence can communicate with human beings.

Artificial intelligence has significantly evolved over the years to a point where understanding human language poses no problem. There are two processing methods that are vital when it comes to developing the structure for machines to understand human speech.

The idea of being able to have conversations with machines must be credited to Alan Turing, whose paper laid the foundations of NLP technology.

What is Natural Language Processing?

Natural Language Processing is a divaricate of computer science; precisely, a branch of artificial intelligence, the act of applying various computational procedures or techniques to perform synthesis and analysis involving speech and natural language.

What is Natural Language Understanding (NLU)?

NLU – Natural Language Understanding is an aspect of Artificial Intelligence that focuses on using computer software to make it possible to understand any form of data through text or speech. NLU is what makes it possible for humans to be able to communicate with machines. Machines can understand the different languages humans use, such as Spanish, English, French, Japanese, etc., and comprehend commands given by humans without having to use the version of computer language, the formalized syntax.

What are the Factors Differentiating NLP & NLU?

NLP and NLU share a lot of things but aren’t at all the same. Many ways in the mode they operate, what they focus on, and their coverage differentiate them from one another. Below are some of the critical differences between NLP and NLU:

  • When discussing NLP and NLU, the most significant difference is that NLU’s area of focus is figuring out the meaning of a sentence. At the same time, NLP emphasizes creating algorithms that can identify and understand the different natural languages
  • NLU makes it possible to comprehend language, while NLP helps machines break down and process language
  • Natural Language Understanding is more concerned and revolves around sentiment analysis which is the process of using text to get information to make it possible for machines to figure out the emotional tones of any reader
  • NLP and NLU receive every data, but NLP is often used instead of NLU when the task is to find patterns in a large data sample that is mainly filled with unstructured data that need to be converted to structured data. At the same time, NLU focuses primarily on changing structured data to unstructured.
  • NLU has a much broader concept, while NLP has a very narrow concept

What Defines the Need of Both NLP & NLU?

We need both NLP and NLU as they work hand in hand to help resolve many human problems. They don’t contradict each other and are similar, making them able to help each other and coexist, making them an excellent choice, especially for companies who want to use AI.

LinkedIn in 2017 added NLP and NLU to its platform. As a result, people could find the content they were looking for with ease, and it successfully created a very conducive environment for its users to use the platform to its best potential.

When are Machines Intelligent?

Machines are intelligent when they can perform the task given to them properly while not in a reliable or stable environment for doing the work. Intelligent machines monitor activities in their environments, adapt and change its action to correspond with the best response for each situation.

NLP & NLU use cases

NLP and NLU are very versatile and widely used in any machine. Below are some of the most popular ways in which they are used:

1.      Message routing and IVR

Interactive Voice Response (IVR) is used for routing calls and self-service. It was mainly about push buttons and didn’t involve any AI during its earlier versions. Still, with its more advanced version being used, NLU and NLP have helped increase its coverage, making it possible for users to communicate with it through voice.

2.      Conversational Chatbots

NLU is one of the driving factors and the primary technology behind conversational chatbots. A conversational chatbot is an automated program that has conversations with humans using natural language through voice or text. Chatbots are usually given a script and can’t divert to anything that isn’t related to the script. They have become essential tools for maintaining good 24/7 customer service for companies.

3.      Grammar Checker

Grammar checking is one of the most prominent and commonly used Natural Language Learning (NLP) applications. Tolls are used to check grammar, observe, find and correct every grammatical error in the text. As a result, NLP helps people learn a language, write a book, audit, etc.

4.      Machine translation

Computers can learn, mature, and adapt due to the AI branch called machine learning. The algorithms used in machine learning make it possible to create text from nothing. With the machine learning algorithm, millions of texts are analyzed by the computer when it comes to translation to learn how to translate text from one natural language to another correctly.

5.      Data capture:

This is assembling and taking note of information concerning a device or an object, event, person, etc. E.g., companies that use NLU in e-commerce can make it possible for customers to input their billing or shipping information through speech. The software then interprets what each customer says and translates the data into words, writing it down.

Some other use cases of NLP and NLU are:

  • Virtual Assistants
  • Sentiment analysis
  •  Search Auto correct, autocomplete
  • Analytics
  • Speech recognition


NLU and NLP have come a long way, and thanks to advancements in technology, they are now used in multiple ways. They are usually used together because that gives the best performance, especially when conversations between two parties are involved. Separating them will only limit the range of activities that you can achieve. NLP works well with any data, but NLU is limited to structured data, meaning that while NLP can have dates or times in its conversations, NLU can’t.

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