A Beginner's Guide to NLP, NLU, and NLG
Artificial intelligence and machine learning methods performing tasks on data are standard worldwide. But, what about human language? Natural Language Processing (NLP), Natural Language Understanding (NLU), Natural Language Generation (NLG) have made a pretty significant splash over the last few years. Still, it is essential not to confuse the three, as their roles are unique.
Many think they fully understand these concepts. After all, one is just processing natural language, understanding it, and generating it, which is in the names itself. However, based on the number of times we see these terms interchanged, we thought it would be beneficial to dive a little deeper.
Let's take a look:
Natural Language Processing (NLP)
Natural language processing occurs when machines read the language. The computer takes the written or verbal text (unstructured data) and converts it to a structured data format.
With machine and deep learning algorithms, NLP software can process and understand the meaning of many variations of human language, written or spoken. These methods are so powerful; they can even recognize intent and emotion within context, and that's where natural language understanding (NLU) comes into the picture.
To fully see the benefits of natural language processing, it is essential to discuss natural language understanding briefly.
Natural Language Understanding (NLU)
NLU is a subset of NLP that detects sentiment and entities and classifies topics. NLU takes the language processed and, like the term suggests, understands it based on the context.
Here is an example: "Should I take a walk?"
NLU will understand that this is a question and the user's intention is to take a walk. The entity is the walk. Through training these models, the computer will understand that there are dependencies for this question.
The bot will realize that a walk is typically taken outdoors and consider the weather when answering. Based on context, the bot will also recognize the meaning of the word take. Because in another scenario, the word may mean "grab" or "steal."
Natural Language Generation (NLG)
Unlike NLP and NLU, NLG produces output. Natural language generation uses structured data and automatically creates human language. NLG gives the ability to tell a story and tell people and companies what their data means in sentences rather than scattered data points.
NLG has been a critical part of our bid automation feature that helps bid writers automate responses to intense questionnaires.
Don't hesitate to contact us to learn more about Mindbreeze and how Mindbreeze InSpire uses NLP, NLU, and NLG to enhance our insight engine.