NLP vs NLU: How Do They Help With Language Processing?

NLP vs NLU comparisons help businesses, customers, and professionals understand the language processing and machine learning algorithms often applied in AI models. It starts with NLP (Natural Language Processing) at its core, which is responsible for all the actions connected to a computer and its language processing system. This involves receiving human input, processing it, and putting out a response. 

However, as discussed in this guide, NLU (Natural Language Understanding) is just as crucial in AI language models, even though it is a part of the broader definition of NLP. Both these algorithms are essential in handling complex human language and giving machines the input that can help them devise better solutions for the end user.

This guide will explore this in greater detail and understand why people confuse the two algorithms as synonymous. We will also examine the differences and similarities between their definitions and applications!

What is the difference between NLP vs NLU?

NLP algorithms are used to understand the meaning of a user’s text in a machine, while NLU algorithms take actions and core decisions. A third algorithm called NLG (Natural Language Generation) generates output text for users based on structured data.

NLU performs as a subset of NLP, and both systems work with processing language using artificial intelligence, data science, and machine learning. With natural language processing, computers can analyze the text put in by the user. In contrast, natural language understanding tries to understand the user’s intent and helps match the correct answer based on their needs.

Check out this detailed guide to learn more about the differences between artificial intelligence and machine learning! In the following sections, we are going to take a look at their applications and purposes:

Purpose

With NLP, the main focus is on the input text’s structure, presentation, and syntax. It will extract data from the text by focusing on the literal meaning of the words and their grammar. The problem is that human intent is often not presented in words, and if we only use NLP algorithms, there is a high risk of inaccurate answers. NLP has several different functions to judge the text, including lemmatization and tokenization.

Using tokenization, NLP processes can replace sensitive information with other values to protect the end user. With lemmatization, the algorithm dissects the input to understand the root meaning of each word and then sums up the purpose of the whole sentence.

With NLU models, however, there are other focuses besides the words themselves. These algorithms aim to fish out the user’s real intent or what they were trying to convey with a set of words.

NLU (Natural Language Understanding) is mainly concerned with the meaning of language, so it doesn’t focus on word formation or punctuation in a sentence. Instead, its prime objective is to bring out the actual intent of the speaker by analyzing the different possible contexts of every sentence.

This intent recognition concept is based on multiple algorithms drawing from various texts to understand sub-contexts and hidden meanings. We will discuss how the two work together in a later section of this guide, but the primary purpose of NLU and NLP joining in at the end of their functions is to give the user the best output depending on the reason for their text input.

Without NLP, the computer will be unable to go through the words, and without NLU, it will not be able to understand the actual context and meaning, which renders the two dependent on each other for the best results. Therefore, the language processing method starts with NLP but gradually works into NLU to increase efficiency in the final results.

Applications

Both language processing algorithms are used by multiple businesses across several different industries. For example, NLP is often used for SEO purposes by businesses since the information extraction feature can draw up data related to any keyword. By accessing the storage of pre-recorded results, NLP algorithms can quickly match the needed information with the user input and return the result to the end-user in seconds using its text extraction feature.

Other popular uses of NLP are in grammar-correcting software, which is used to edit and summarise text after giving relevant suggestions to autocorrect any errors the user can disregard or accept depending on their intent with the text.

As the basis for understanding emotions, intent, and even sarcasm, NLU is used in more advanced text editing applications. In addition, it can add a touch of personalization to a digital product or service as users can expect their machines to understand commands even when told so in natural language.

With intent classification features, NLU algorithms can go through emails, documents, and other messages to understand a customer’s intent from different sentences and keywords. This insight is invaluable for business decisions about marketing or customer care as they thoroughly analyze customer behavior.

Since NLU can understand advanced and complex sentences, it is used to create intelligent assistants and provide text filters. It is also used to provide predictive text suggestions in modern software. For instance, it helps systems like Google Translate to offer more on-point results that carry over the core intent from one language to another.

What is NLP best used for?

Natural language processing is best used in systems where focusing on keywords and working through large amounts of text without focusing on sentiments or emotions is essential. It all comes down to breaking down the primary language we use every day, and it has been used across many products for many years now. Some common examples of NLP applications include editing software, search engines, chatbots, text summarisation, categorization, mining, and even part-of-speech tagging.

Other uses include email monitoring and language modeling. For example, NLP can pick out specific keywords of the subject lines with named-entity recognition in emails and automatically place them in sections like Spam Mail or Primary Mail. In reality, modern tools use both NLU and NLP solutions in a hybrid solution, meaning each has a degree of semantic and syntactic analysis.

However, syntactic analysis is more related to the core of NLU examples, where the literal meaning behind a sentence is assessed by looking into its syntax and how words come together.

What is NLU best used for?

Natural language understanding is the leading technology behind intent recognition. It is mainly used to build chatbots that can work through voice and text and potentially replace human workers to handle customers independently. One famous example of NLU usage is Apple’s Siri assistant.

Since it is a subfield of NLP, many of their functions tie in together. Still, NLU is based on sentiment analysis, as in its attempts to identify the real intent of human words, whichever language they are spoken in. This is quite challenging and makes NLU a relatively new phenomenon compared to traditional NLP.

Another benefit of these algorithms is the improvement and seamlessness they bring to machine-human interaction, despite any language barriers, enabling computers to comprehend the meaning of the input and answer in the user’s language. Some other common uses of NLU (which tie in with NLP to some extent) are information extraction, parsing, speech recognition, and tokenization.

How can companies use NLP and NLU together?

While it is true that NLP and NLU are often used interchangeably to define how computers work with human language, we have already established the way they are different and how their functions can sometimes submerge.

The reality is that NLU and NLP systems are almost always used together, and more often than not, NLU is employed to create improved NLP models that can provide more accurate results to the end user. No rule forces developers to avoid using one set of algorithms with another. As solutions are dedicated to improving products and services, they are used with only that goal in mind.

NLP is the more traditional processing system, whereas NLU is much more advanced, even as a subset of the former. Since it would be challenging to analyze text using just NLP properly, the solution is coupled with NLU to provide sentimental analysis, which offers more precise insight into the actual meaning of the conversation. Online retailers can use this system to analyze the meaning of feedback on their product pages and primary site to understand if their clients are happy with their products.

Suppose companies wish to implement AI systems that can interact with users without direct supervision. In that case, it is essential to ensure that machines can read the word and grasp the actual meaning. This helps the final solution to be less rigid and have a more personalized touch.

Since modern chatbots utilize both NLP and NLU systems for customer service, they can provide far better results. This means that they can guide a user about the actual topic of their query and act based on the changing context of the conversation by analyzing every sentence for its literal meaning and overall emotion!

Conclusion

Language processing is the future of the computer era with conversational AI and natural language generation. NLP and NLU will continue to witness more advanced, specific and powerful future developments. With applications across multiple businesses and industries, they are a hot AI topic to explore for beginners and skilled professionals.

Technology will continue to make NLP more accessible for both businesses and customers. Book a career consultation with one of our experts if you want to break into a new career with AI.