NLP, NLU and NLG are all forms of artificial intelligence technology. All three of them deal with human language, but are involved at different points in the process and for different reasons. What is the difference between NLG, NLP and NLU? Each technology is different in its own right and understanding them may be difficult at first. This article will guide you through the differences in each technology and give you an understanding of how each is used.
These are the three basic acronyms used most frequently when discussing language related AI-technology:
- NLP – Natural Language Processing
- NLU – Natural Language Understanding
- NLG – Natural Language Generation
Employing these technologies builds conversationally intelligent applications that can carry out engaging dialogues with humans. Companies are spending millions of dollars to research these technologies and explore their applications. Let us explore each one and discover their real-world applications.
NLP (Natural Language Processing)
NLP is the umbrella term that describes the ability to break down what the human says, understand what it means, decide on an appropriate action and form a response delivered in comprehensible language back to the human. Both NLG and NLU are closely related to NLP, which provides the processing ability to the two other technologies.
The effectiveness of an NLP system depends on its ability to understand what the human has said and then comprehend it correctly. As the system learns and grows, its understanding of human language and thought processes becomes more and more natural and life-like.
NLU (Natural Language Understanding)
Sometimes used interchangeably with NLP, NLU is actually a subset of the understanding and comprehensions portion of NLP. NLU is actually the portion of the technology that interprets human language into machine language. This is the first step in processing; understanding what the human is saying, what they are instructing the machine to do or the question they are asking.
Humans are typically able to understand each other when they speak the same language. This is true even when words are mispronounced or slang is used. Machines, on the other hand, are better at predictable situations.
NLG (Natural Language Generation)
Human language offers an infinite way of saying the same thing in different ways. Human language never conforms to a defined script or format. A single human may, at different times, type different messages to express the same idea. Sometimes human language is abstract or ambiguous, especially during regular conversations.
NLG systems respond to human language with intelligent, clear responses that are useful and make sense. Through the latest deep learning algorithms, NLG systems are able to comprehend questions and problems and respond with intelligent answers. Simply put, NLG generates text from data that has been processed through NLP and NLU.
The process of communication
Let us break it down further by thinking of this technology in human terms. Imagine your significant other asks you to go buy some milk. One of the questions you would ask is how much milk do they want; one gallon, one pint? They tell you they want one gallon of milk, so you check the price at a few stores, ask for the correct amount of money and go off to buy the “right” milk.
NLP, NLU and NLG processes follow the same line of thinking but have two differences, which are how and who you are communicating with. Here is the same scenario from an AI perspective:
You find a milk advertisement on social media and send a chat message that you would like some. NLP kicks in to change your request to data codes that a machine can understand. NLU takes it from there and knows it must meet certain criteria. In this case, it needs to get the information on how much milk you want. NLG creates a question based on this criteria asking you how much milk you want in human language.
When you respond, NLP goes back into decision mode again and analyzes the product and quantity. The machine figures in your location and finds the cost information. The machine then responds with options on where to purchase. Once you confirm, the machine continues with your transaction and you have milk on the way.
NLG – more than just a chatbot
A robust NLG system effectively and efficiently communicates with your customers in a messaging situation, as described in the milk example. Additionally, NLG can be deeply involved in a company’s marketing, for instance creating content for web-pages.
In a traditional sense, marketing technology uses algorithms that are human controlled. In an AI environment, the machine uses algorithms and increases marketing potential with much faster processing power. Perceptive marketers are employing this technology for automation of product descriptions, data-driven articles and posts as well as analytical reports.
Phrasetech is the leading company in Natural Language Generation technology. Our solution provides enterprise companies with tools to create meaningful, unique content at a large scale, effectively improving the overall customer experience.