Artificial intelligence is becoming an important part of our lives. However, when it comes to understanding human language, technology still isn't at the point where it can give us all the answers.
That's why companies are using natural language processing to extract information from text. With AI and machine learning, NLU(natural language understanding), NLP (natural language processing), and NLG (natural language generation) have played an essential role in understanding what user wants.
Furthermore, NLU and NLG are parts of NLP that are becoming increasingly important. These technologies use machine learning to determine the meaning of the text, which can be used in many ways.
So, What is the difference between NLU and NLP? To understand this, we first need to know what each term stands for.
In this article, we have highlighted the difference between NLU and NLP and how they are important.
What is Natural Language Processing?
NLP is the computational subfield of natural language processing, which aims to model natural human language data.
It is commonly used in computer science, information systems, linguistics, communications, and philosophy.
NLP has many subfields, including computational linguistics, syntax analysis, speech recognition, machine translation, and more.
Natural language processing works by taking unstructured data and creating a model of it. It works by building the algorithm and training the model on large amounts of data analyzed to understand what the user means when they say something.
What is natural language understanding?
NLU is a subset of NLP that uses the semantic analysis of text to understand the meaning of sentences.
NLU algorithms process text from different sources using computational methods to reach some understanding of an input text, which is as simple as understanding what a sentence says or as complex as understanding dialogue between two people. For example, it is the process of recognizing and understanding what people say in social media posts.
So, NLU uses computational methods to understand text and produce a result.
NLU can be used in many different ways, including understanding dialogue between two people, understanding how someone feels about a particular situation, and other similar scenarios.
Do we need both NLP & NLU?
Yes, NLU and NLP work together.
They work together to create more complex NLP models. A common example of this is sentiment analysis, which uses both NLP and NLU algorithms in order to determine the emotional meaning behind a text.
While both NLP and NLU are related, they are different in their aims. NLP models are designed to describe the meaning of sentences whereas NLU models are designed to describe the meaning of text in terms of concepts, relations and attributes.
NLP vs NLU: What’s the difference?
The major difference between the NLU and NLP is that NLP focuses on building algorithms to recognise and understand natural language, while NLU focuses on the meaning of a sentence.
Both NLU and NLP use supervised learning, which means that they train their models using labeled data. However, the difference between them is in how it's done.
Another difference between NLU and NLP is that NLU is focused more on sentiment analysis. Sentiment analysis involves extracting information from the text in order to determine the emotional tone of a text.
NLP & NLU use cases
Only 20% of data on the internet is structured data and usable for analysis. The rest 80% is unstructured data, which can't be used to make predictions or develop algorithms.
That's where NLP & NLU work together to ensure that the huge pile of unstructured data is made accessible to AI.
Machines help find patterns in unstructured data, which then help people in understanding the meaning of that data. This is how NLP & NLU use cases are being implemented today.
Some common use cases of NLP & NLU are:
1. Personal assistants
2. Image and video recognition
3. Content moderation
NLP and NLU together
NLP and NLU work together to help solve a number of problems. They both offer similar benefits, which makes them an excellent choice for any company looking to leverage AI.
To help you understand the advantages of NLP and NLU together, let's take a look at some use cases:
In 2017, LinkedIn expanded its AI capabilities by integrating NLP & NLU into their platform. This allowed it to provide relevant content for people who were interested in specific topics. This allowed LinkedIn to improve its users' experience and enable them to get more out of their platform.
NLP & NLU works in various industries together:
1. Finance & Banking
2. Media & Entertainment
3. Government & Healthcare
4. Retail & Consumer Goods
5. Automotive & Transportation
What is the future of natural language?
Pursuing the goal to create a chatbot that can hold a conversation with humans, researchers are developing chatbots that will be able to process natural language.
The ultimate goal is to create an intelligent agent that will be able to understand human speech and respond accordingly.
That's why NLU, NLP and NLG work together. In fact, A combination of NLU and NLG gives an NLP system.
However, there are still many challenges ahead for NLP & NLU in the future. One of the main challenges is to teach AI systems how to interact with humans.
NLP and NLU are used in a lot of different ways, but they're usually interlinked. If you're building an app that will have a chat function, then the two terms should be familiar to you.
The difference between them is that NLP can work with just about any type of data, whereas NLU is limited to structured data. In other words, NLU can use dates and times as part of its conversations, whereas NLP can't.
It's also important to remember that although both NLP and NLU are used for conversational apps, they have their own uses as well.