What are NLP, NLU, and NLG, and Why should you know about them and their differences?

Artificial Intelligence and its applications are progressing tremendously with the development of powerful apps like ChatGPT, Siri, and Alexa that bring users a world of convenience and comfort. Though most tech enthusiasts are eager to learn about technologies that back these applications, they often confuse one technology with another.

NLP, NLU, and NLG all come under the field of AI and are used for developing various AI applications. However, all three of them are distinct and have their purpose. Let us know more about them in-depth and learn about each technology and its application in the blog.

What are NLP, NLU, and NLG?

NLP (Natural Language Processing)

Nlp (natural language processing) It is a field of Artificial Intelligence that enables machines to understand and process human language. It analyzes large amounts of textual and speech data, identifies patterns, and generates intelligent responses.

To understand more comprehensively, NLP combines different languages and applications, such as computational linguistics, machine learning, rule-based modeling of human languages, and deep learning models.

When all these models are processed together and facilitated with data in voice or text form, it generates intelligent results, and the software becomes capable of understanding human language.

Additionally, the models now being developed are assisted more carefully than before, and processes like speech recognition, word sense disambiguation,  speech tagging, sentiment analysis, and natural language generation are leveraged that help in generating more accurate user responses and make NLP applications more refined.

Applications of NLP

Some of the top applications of NLP include:

  • Voice-operated GPS System.
  • Digital Assistants.
  • Speech-to-Text Dictation.
  • Virtual Assistants like Alexa, Siri, etc.

NLP fundamentally performs these three tasks to ensure the success of their applications:

  • Translation of text from one language to another.
  • Summarization of large data and text in real-time.
  • Responding to the commands of users.

[Also Read: 15 Best NLP Datasets to train you Natural Language Processing Models]

Nlp solutions datasets

NLU (Natural Language Understanding)

Nlu (natural language understanding) It is a subfield of NLP that focuses on interpreting the meaning of natural language to understand its context better using syntactic and semantic analysis. Some of the most common tasks included in NLU are:

  • Semantic analysis
  • Intent recognition
  • Entity recognition
  • Sentiment analysis

The syntactic analysis NLU uses in its operations corrects the structure of sentences and draws exact or dictionary meanings from the text. On the other hand, semantic analysis analyzes the grammatical format of sentences, including the arrangement of phrases, words, and clauses.

Humans have the natural capability of understanding a phrase and its context. However, with machines, understanding the real meaning behind the provided input isn’t easy to crack.

Hence, the software leverages these arrangements in semantic analysis to define and determine relationships between independent words and phrases in a specific context. The software learns and develops meanings through these combinations of phrases and words and provides better user outcomes.

Applications of NLU

Here are a few applications of NLU:

  • Automated Customer Service Systems.
  • Intelligent Virtual Assistants
  • Search Engines
  • Business Chatbots

NLG (Natural Language Generation)

Nlg (natural language generation) It is a subfield of NLP which focuses more on generating natural language from structured data. Unlike NLP and NLU, NLG’s primary purpose is to create human language responses and convert data into a speech format.

NLG uses a three-phase system to ensure its success and provide precise outputs. Its language rules are based on morphology, lexicons, syntax, and semantics. The three phases it uses in its approach are:

  • Content Determination

    In this phase, the NLG system determines what content should be generated based on the user inputs and corrects it logically.

  • Natural Language Generation
    At this stage, the punctuation, text flow, and para breaks of the content generated in the first phase are checked and corrected. Moreover, pronouns and conjunctions are also added to the text wherever required. 
  • Realization PhaseBeing the last phase of NLG, grammatical accuracy is rechecked. Also, the text is checked to see if it correctly follows punctuation and conjugation rules.

Applications of NLG

Here are some of the applications of NLG:

  • Business Analytical Intelligence
  • Financial Forecasting
  • Customer Service Chatbots
  • Summary Generation

What is the Difference Between NLP, NLU, and NLG?

As mentioned at the start of the blog, NLP is a branch of AI, whereas both NLU and NLG are subsets of NLP. Natural Language Processing aims to comprehend the user’s command and generate a suitable response against it.

NLU, on the one hand, can interact with the computer using natural language. NLU is programmed to decipher command intent and provide precise outputs even if the input consists of mispronunciations in the sentence.

NLG, on the other hand, is above NLU, which can offer more fluidic, engaging, and exciting responses to users as a normal human would give. NLG identifies the essence of the document, and based on those analytics, it generates highly accurate answers.


Summing up, NLP converts unstructured data into a structured format so that the software can understand the given inputs and respond suitably. Conversely, NLU aims to comprehend the meaning of sentences, whereas NLG focuses on formulating correct sentences with the right intent in specific languages based on the data set. Refer to our Shaip experts to learn about these technologies in detail.

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