What is Natural Language Understanding (NLU)? Who is going to understand the natural language of us humans? Why is it important for professionals to work on understanding natural language? What is the use of understanding natural language in natural language processing?

ChatGPT talks to us humans, just like a human. He understands our questions. If something is unclear to him, he asks correct and accurate questions to clear up his confusion. That chatbot, that machine, understands our language to a very high and acceptable level. The same goes for voice assistants (like Siri & Alexa). They also understand what we are saying.

How is it possible that an inanimate being, a machine that understands the language of zeros and ones and codes, understands the language of living beings and communicates with them in the same language and answers their questions?

If it wasn’t for artificial intelligence, if it wasn’t for machine learning, if it weren’t for natural language processing (NLP) experts, and if it weren’t for the very important subfield of artificial intelligence called natural language understanding; No machine could listen, read, understand and respond to the natural language of humans.

This content is about NLU and answers the questions that were raised in the beginning.

What is Natural Language Understanding?

The purpose of artificial intelligence is to simulate the functions of the human brain and its cognitive abilities. If we look at the history of artificial intelligence, we find that experts from different fields were involved in the development of artificial intelligence to make machines that look like humans (humanoid robots).

Understanding language is one of the most important human cognitive abilities. Therefore, the project of simulating human intelligence is not complete without teaching machines to understand language. Natural language understanding is a branch of artificial intelligence that aims to make machines imitate human speech.

simplilearn has provided a very simple definition of natural language understanding.

Natural Language Understanding (NLU) is the ability of a computer to understand human language.

Components of a natural language understanding system

The natural language understanding process has 3 stages. In these 3 steps, the software takes an input (text or verbal sentence) and delivers the requested output. But to understand the steps correctly and completely, it is necessary to give explanations about the design and training of natural language understanding systems or models and their elements.

With the help of Python language and machine learning, experts design and train the algorithm (model) for this work. Natural language understanding systems are usually built for commercial purposes. The best example is chatbots that businesses build and train for customer service or support. These chatbots have a specific vocabulary (lexicon). For example, if the chatbot is supposed to guide and accompany the user to buy a plane ticket, his vocabulary and sentences are related to the same subject and the same business, and he cannot be asked about the sports news of the day or the weather forecast.

Therefore, each system needs a dedicated dictionary to use to find the meaning of words. Also, he must have the grammar rules to be able to understand the role of each word in the sentence. And finally, rules for categorizing concepts (Ontology) must have been taught to it. For example, categories of people, places, brands, etc. are defined for the system.

With the help of the same rules, the system can put each word in its category and understand the relationship between the categories (concepts) and consequently the main meaning of the sentence. These elements and components are placed together so that the system has the necessary data and information to analyze each input (sentence) and understand its meaning and emotional load in the following steps.

The 3 stages of NLU are:

1. Tokenization

The first step is for the system (algorithm) to divide the input into its components. Each sentence component (words and punctuation marks) is a token for the software.

2. Lexical Analysis

In the second step, it determines the grammatical identity of each token (noun, adjective, verb, adverb, etc.) with the help of its dictionary.

3. Syntactic Analysis

In the third step, the grammatical role of each token in the sentence is determined so that the connections between sentence components are clear for the system. This step is very important.

Let us explain the importance of this step with an example. The entry is this sentence: I am upset because my order is not registered. Well, first the system interprets each word for itself and understands what each word means. At this stage, the nerve is a noun, and to break is a verb that means to break and has nothing to do with nerves.

If the third step is not used and the system does not consider the role of words in the whole sentence and does not deal with the second verb and the relationship between the first and second sentences and the syntactic structure of the sentence, it cannot understand the dissatisfaction and negative feelings of the speaker. An important thing to note about natural language understanding is that the system is not designed to understand only the meaning of a single sentence. The system reaches the speaker’s feelings by analyzing the overall structure of the sentence.

Just like you and me, if we hear that sentence, we realize that the speaker is upset and angry. The system also understands the meaning of eating nerves according to the reason (negative present tense) that comes after the preposition like.

So, by using those components and going through these steps, the system understands what the user says, what he means, what feelings he has, and what answer should be given to him.

The most important applications of NLU

Natural language understanding systems have taken the place of human resources in many businesses and collections to perform repetitive questions and answers.

  • Interactive Voice Responding or IVR: In this application, the system is designed and trained based on the questions and needs of business customers or visitors to the collection. In IVR it is the NLU that answers and directs the caller to the appropriate department or expert.
  • After-sales service to customers: Chatbots use natural language understanding systems. In this application, they train the algorithm with a series of questions and sequential answers in each business.
  • Data capture: NLU system is used to collect and record data from users. For example, a business may have designed a natural language understanding model to place orders over the phone.
  • Conversational interfaces: examples of this application are voice assistants (Amazon Alexa) and Google Home. These systems can understand and respond to voice commands spoken by users in natural language.


As mentioned at the beginning, natural language understanding is a subset of natural language processing.

Natural language processing is a set of technologies and techniques that translate the human language for the machine so that the machine can understand it and extract the information from it and make it available to humans.

Natural language processing is divided into two parts: natural language understanding and natural language generation (NLG). Understanding natural language is the first step. It can be said that it is understanding. First, the computer must understand what the human means so that it can answer. Natural language generation is a written response that the system gives in natural language to the user’s written question. The best example of natural language generation is ChatGPT.

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