NLP vs NLU vs NLG: Differences and Applications

NLP vs NLU vs NLG: Differences and Applications


Communication is all about language and how people convey their thoughts to each other through stories and instances. With the advent of technology and Artificial Intelligence becoming the new ‘source of language,’ communication is not restricted merely to humans interacting with humans but now means humans interacting with machines. 

This form of communication is not easy and requires precision and technology to convert smoothly from one language format to another. This task of automatically transforming data into English language content and vice versa is performed by combining three computational software processes: Natural Language Processing, Natural Language Understanding, and Natural Language Generation. 

Let us delve deeper to know what they are? What do they do? What are their applications? And how are these three different from each other?

Today we spend a lot of time engaging in conversations over texts, emails and social media. But have we ever thought about the data we generate every day? This data is mostly unstructured and it is hard for computers to understand. As businesses are growing fast, processing this large amount of information is becoming more time consuming and repetitive. Fortunately, Natural Language Processing tools are helping discover helpful information from these voluminous amounts of data. So how are these Natural Language Processing tools proving beneficial for customers and businesses? Let’s have a look!

What is Natural Language Processing?

what is NLP - Natural language Processing

  1. Natural Language Processing is a branch of Artificial Intelligence that strives to give computers the ability to understand and respond to text or voice data in the same way humans do.
  2. NLP is a combination of rule-based linguistics along with statistical, machine learning models. These combine to enable computers to process and understand the whole meaning of human intent and sentiment.
  3. NLP drives computer programs to translate text, summarise large volumes of textual/verbal data and respond to commands in real-time.
  4. NLP helps in streamlining business operations, increases employee productivity, and helps in simplifying mission-critical business processes.

What tasks does NLP perform?

  1. NLP enables speech-to-text conversion. This is a tricky task as speech recognition emphasizes spoken questions that have differing accents, intonations, and often incorrect grammar.
  2. NLP also performs the task of word sense disambiguation by selecting the correct meaning of the word, which has multiple meanings to make the most sense in a given context.
  3. NLP can also do a sentiment analysis by extracting subjective qualities of attitudes, emotions, and suspicions from the text. 
  4. NLP enables marketers to create effective strategies by learning more about their customers by analyzing topics and understanding customer sentiment.

Applications of NLP

  1. NLP is useful for businesses that deal with large unstructured forms of data. NLP helps companies analyze this data and gain meaningful insights from it to drive business decisions. It helps in understanding customer preferences to make smarter decisions.
  2. NLP helps in keyword extraction by detecting specific and relevant information from the text.
  3. NLP automates text summarization by deriving important information and key phrases from the text.
  4. NLP is a vital tool to check grammatical errors and improve one’s writing skills.
  5. NLP helps detect the urgency of text by recognizing certain words and expressions that denote the text’s gravity. This helps in prioritizing important requests, thus improving the response and efficiency of the machine.

What is Natural Language Understanding?

NLG- natural language generation

  1. Natural Language understanding uses computer software to understand input fed in the form of text or speech. 
  2. It comprehends human language and allows the computer to understand commands without a formal syntax. 
  3. Computers can communicate with humans in their natural languages. 
  4. NLU also enables speech recognition. 
  5. Siri is an NLU application working towards human-computer interaction.
  6. It is a branch of artificial intelligence that allows human-computer interaction. 
  7. It helps in the creation of chatbots that can interact with humans without any external supervision.

How does NLU work?

  1. Natural Language Understanding analyses the data fed into the computer. 
  2. They determine the meaning of the data by using algorithms. 
  3. The fundamental concepts of NLU are intent recognition and entity recognition.
  4. Intent recognition helps identify users’ sentiments in the input they have given and determine its objective, helping to establish the text’s meaning.
  5. Entity recognition identifies named and numeric entities in the messages and then categorizes them into different groups.

Applications of NLU

  1. NLU has speech recognition software that works towards enhancing human-computer interactions. 
  2. It has an Interactive Voice Response (IVR) and message routing system that processes human voice into text, frames it into grammatically correct sentences, and determines the users’ intent.
  3. The NLU technology in chatbots enhances customer support and services by conversing with humans in natural language. Chatbots have a scripted language designed using different processes of feature extraction, entity linking, and knowledge management.
  4. NLU helps in capturing data. The software gathers and records information about different objects, persons, and events.
  5. NLU allows conversational interfaces to respond to human language using semantic knowledge to infer the intent of the text.

What is Natural Language Generation?

what is NLG - Natural Language Generation

  1. NLG is a subfield of Artificial Intelligence that performs the task of transforming data into English content.
  2. It is an innovative way to analyze and interpret vast amounts of data cost-efficiently, keeping in mind customer satisfaction.
  3. NLG deals with language and handles the insights produced by data through automated forms of analysis.
  4. NLG is an intelligent system that performs rules-based functions having pre-existing templates and expresses important concepts within data in a much more straightforward, relevant, and intuitive language.

What does NLG do?

  1. In its standard form, Computer-generated text is not very fluid. It lacks emotion and personality as well. Natural Language Generation uses NLP to help computers produce human-like text to make the content more exciting and engaging.
  2. NLG helps in automating routine tasks and communication analysis for companies. This increases productivity, and a company’s human resources can be diverted to focus more on high-value activities.
  3. NLG helps create content related to writing reports, stories, and articles and conveniently making it sound like human language. All this helps in reducing operational costs and increasing revenues.

Applications of NLG

  1. Chatbots and virtual assistants utilize NLG to answer customer queries. They are designed to understand and process human language and deliver appropriate responses. Chatbots learn from interactions and improve over time.
  2. NLG is about having conversations with systems that have data. It helps in deepening the understanding of jobs, businesses, devices, and much more. With NLG, the information is the same but interaction is more natural. 
  3. NLG makes it easier to answer questions about products and services to resolve purchasing issues, receive fast responses and achieve a high degree of accuracy.

NLU v/s NLP v/s NLG


  1. NLU is a subset of NLP and helps in carrying out a dialogue with computers using natural language.
  2. NLU can communicate with untrained individuals, understand the intent of the words and interpret their meaning despite human errors of mispronunciation or transposed words.
  3. NLG is also a subset of NLP. It enables computers to generate natural language text, thereby imitating the way humans interact. This is a departure from the computer-generated text. 
  4. According to Gartner’s Hype Cycle for BI and Analytics, “NLP focuses on deriving analytic insights from textual data whereas NLG is used to synthesize textual content by combining analytic output with contextualized narratives.”
  5. NLP performs the reading function whereas, NLG performs the writing function. 
  6. By looking at the language of text, NLP tries to figure out the ideas being communicated. NLG starts with a set of ideas presented in the text and turns them into a communicable language.


With advancements in technology, features like NLP, NLU and NLG are broadening their capabilities and interacting with users via phone systems. They are helping businesses analyze data, automate time-consuming processes, and help gain a competitive advantage over their competitors. Today machine intelligence is being driven by Natural Language Processing to address modern real-world applications. NLP provides solutions to many problems ranging from spam detection, machine translation, sentiment analysis, and text summarization. To sum it up, these language processing tools have a lot to offer, and SaaS tools such as chatbots are the most accessible way to get started with them.

BotPenguin is a chatbot creator platform that lets you create NLP based chatbots that can interact with customers like human beings. Try BotPenguin’s chatbot now to grow your business and fully automate customer experience.

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