What is Natural Language Understanding (NLU)?

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When we think about it, language is one of our most powerful tools. We use it to express our feelings and thoughts. We can leverage the power of language to convey our thoughts, persuade someone, or even make someone laugh.

But what if we could use language in a way that allows us to understand what someone else is thinking?

In the world of artificial intelligence, Natural Language Understanding (NLU) has become one of the prominent topics. We already know that computers can do incredible things; however, there is still much work to be done when it comes to understanding human speech. 🛣️

This is where NLU comes in, aiming to change the status quo by making it easier for computers to understand how humans communicate.

In this article, we will dive into what Natural Language Understanding is. So, let’s get started…🏄

What is Natural Language Understanding (NLU)?

Natural Language Understanding (NLU) is a subfield of Natural Language Processing (NLP) that focuses on enabling machines to understand and interpret human language. Of course, it has different characteristics compared to natural language processing.

NLU is particularly concerned with understanding the meaning, context, and intent behind the words and phrases used by people.

You might be wondering, "What exactly does NLU do?" Essentially, NLU deals with transforming unstructured language data into structured information that machines can work with.

In other words, it allows for identifying words in a sentence, determining the sentiment of a statement, or classifying the intent behind a user’s query.

Let's make it clearer with an example. 👇

Imagine you ask an AI, "What will the weather be like tomorrow? Should I bring my umbrella?" In order for the machine to extract the meaning and context, it must rely on NLU. ☔

First, the user's intent is identified. In this case, the intent is determined to be "weather inquiry."
The second step is entity extraction.

"Tomorrow" → Date (Future day)
"Umbrella" → Object
The third step is context understanding. Here, it becomes clear that the user wants to know whether they should bring an umbrella depending on the likelihood of rain.

Based on this, the system’s response could be, "Rain is expected tomorrow, so you should bring your umbrella."

It’s not just about providing weather information; NLU has broad implications in various sectors, from improving customer service through chatbots to healthcare and education. In this example, NLU allows the system to comprehend the context of language and generate an appropriate response.

How Does Natural Language Understanding (NLU) Work?

Going beyond speech recognition, NLU uses machine learning models that improve over time. These models are trained on relevant training data that helps them recognize patterns in human language.

Training data used for NLU models typically includes human language examples. A good example is customer chat logs.

The first step in NLU is preprocessing textual data. This includes tasks like tokenization, which breaks the text into individual words or phrases, or part-of-speech tagging, where each word is labeled with its grammatical role.

After preprocessing, NLU models use various machine learning techniques to extract meaning from the text.

There are two common approaches used here: Intent Recognition and Entity Recognition.

Intent Recognition defines the user's sentiment and identifies their purpose, helping determine the meaning behind the text. 💬

Entity Recognition focuses on identifying the entities in a message and extracting relevant information about them. Entities can be named (people, companies, locations) or numerical (numbers, currencies, percentages). 🔤

Applications of Natural Language Understanding

Human language is quite complex for computers to understand, which is completely understandable. When we speak, we don’t usually think about how fluid, continuous, and intricate our language is.

What’s interesting is that two people can read the same text and interpret it in entirely different ways depending on their understanding, values, philosophy, or perspective.

If humans can interpret the same language differently due to inherent linguistic challenges, you can imagine how difficult it must be for machines to work with unstructured data.

This is where NLU stands out. This technology enables different systems to achieve a level of comprehension similar to humans. It even has the capability to detect spelling mistakes and correct mislabeling.

As a result, NLU is used in a wide range of areas such as email responses, customer service, sentiment analysis, and voice assistants.

Here are some of the key applications of NLU: 👇

Chatbots

One of the primary applications of NLU is in chatbots and virtual assistants. These systems use NLU to accurately interpret and respond to user requests.

For instance, when you ask Alexa to set a 10-minute timer, the device will use NLU to understand your request and then initiate the timer setting process.

In short, NLU is essential not only for understanding individual queries but also for maintaining the flow of conversations. It enables chatbots and virtual assistants to track context, allowing for more natural and consistent dialogues.

For example, if a user speaks angrily to a chatbot, the machine will recognize this and respond accordingly. This is exactly what NLU enables in this context.

Sentiment Analysis

Sentiment analysis is another important application of NLU, commonly used to assess customer feedback and opinions.

Businesses use NLU to analyze the sentiment expressed in customer reviews, social media posts, and other forms of feedback to determine whether the overall sentiment is positive 😊 or negative 😡

Text Classification

NLU plays a crucial role in text classification tasks like spam filtering. NLU systems can effectively distinguish between legitimate emails and spam by analyzing the content and intent behind emails or messages.

Two popular tools for text classification are SpaCy and NLTK.

SpaCy is an open-source library designed for efficient and fast NLP in Python. It provides pre-trained models for various text classification tasks.
NLTK is another powerful Python library that provides easy-to-use tools for tasks like tokenization, part-of-speech tagging, and text classification.

What is the Difference Between Natural Language Processing (NLP) and Natural Language Understanding (NLU)?

Contrary to popular belief, Natural Language Understanding (NLU) is not in competition with Natural Language Processing (NLP). Instead, both fall under the same umbrella.

It’s important to note that NLU is not separate from NLP but rather a critical component within it.

Although people often use the term “NLU vs NLP,” this comparison is misleading because NLU is a subset of NLP.

The only meaningful way to compare the two would be to look at NLP tasks that do not fall under the scope of NLU. 👀

While NLP covers a wide range of tasks related to text processing and analysis, NLU focuses specifically on understanding language. For example, NLP might include tasks like speech recognition or text generation, whereas NLU deals with understanding the meaning behind the input text. In other words, NLU is concerned with understanding what the user is trying to say and why they are saying it.

Let’s clarify this distinction with an example:
Imagine a user asks AI, "I’ve got a mountain of homework, what should I do?" 😀

While NLP might interpret the phrase "mountain of homework" literally and offer suggestions related to geography or climbing ⛰️, NLU will understand that the user is expressing that they have a lot of homework and are probably looking for help or solutions.