NLP vs. NLU: From Understanding to its Processing

In recent years, with so many advancements in research and technology, companies and industries worldwide have opted for the support of Artificial Intelligence (AI) to speed up and grow their business. AI uses the intelligence and capabilities of humans in software and programming to boost efficiency and productivity in business.

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However, when it comes to handling the requests of human customers, it becomes challenging. This is due to the fact that with so many customers from all over the world, there is also a diverse range of languages. At this point, there comes the requirement of something called ‘natural language’ in the world of artificial intelligence.

Natural language, also known as ordinary language, refers to any type of language developed by humans over time through constant repetitions and usages without any involvement of conscious strategies.

In AI, two main branches play a vital role in enabling machines to understand human languages and perform the necessary functions. These branches are NLP and NLU.

So, what are NLP and NLU? What is their connection? What are their uses? What is the difference? Let’s find out in this NLU vs. NLP article today!

What is NLP?

NLP, short for Natural Language Processing, is a branch of AI that concerns using computational programming methods to make machines develop the ability to recognize, analyze, decipher, or generate both the verbal and non-verbal forms of natural human language and convert them into data to derive insights and information.

NLP has three main categories, which include:

• Text Processing: This category involves building and programming all the chatbots and other virtual assistants with the help of machine learning codes. The technique is dependent on several gigabytes of data, which are derived from written human conversations.
• Speech Recognition: This category enables machines to handle and convert sounds of immediate speeches into words and recognize and understand them.
• Speech Synthesis: The opposite of Speech Recognition, Speech Synthesis involves enabling machines to produce sounds or speak words.

While sentences are divided into words or linguistic phonetics in the case of text processing and speech recognition, these words or phonetics are gathered and repositioned in speech synthesis to make machines or robots speak sentences.

NLP helps with:

• Recognizing and Generating Speech
• Tokenization
• Taking apart or Parsing
• Extracting Information
• Finding Similarities

What is NLU?

NLU, short for Natural Language Understanding, is a subfield of NLP, which concerns teaching machines to identify, extract, interpret, and understand the textual and non-textual forms of natural human language and convert them into data that can be used for performing further functions.

These three main categories for understanding natural language include:

• Syntactic Knowledge: Understands and checks the sentences, grammar, phrases, etc. of the text or speech.
• Semantic Knowledge: Recognizes, derives and validates the meaning of words and sentences in a text or speech.
• Pragmatic Knowledge: Understands and checks the contextual aspects of the text in order to determine the motive behind the text or speech.

NLU helps in performing small tasks like:

• Understanding conversation between two people.
• Validating how an individual feels in a specific situation.
• Categorizing Texts.
• Gathering News.
• Arching Single Texts.
• Analyzing Content.
• Issuing Commands.
• Redirecting emails to the right person.

NLU vs. NLP: What is the connection?

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As already seen in the above information, NLU is a part of NLP and thus offers similar benefits which solve several problems. In other words, NLU helps NLP to achieve more efficient results by giving a human-like experience through machines.

Due to this, they are capable of working collectively together in various important fields, companies, industries, businesses, and organizations, which involve AI to manage the high volumes of data produced on a daily basis. The common ones include:

• Public relations and mass media.
• Advertising Industries.
• Gaming & Entertainment.
• Banking & Finance.
• Manufacturing & Production Industries.
• Medical attention.
• Travel Industries.
• Transportation & Logistics.
• Retail and E-Commerce.

NLP vs. NLU: What is the use of them?

There are a lot of uses of NLP and NLU. The major ones are listed below:

In real life, NLP is used for:

• Text Summarization
• Category of Text
• Text Exploration
• Sentiment Analysis:
• Extraction of a Topic
• Entity Recognition by Name
• Tagging Parts of Speech
• Relationship Extraction is a term for
• Stemming from
• Machine Translation
• Automatic Question Answering
• Population of Ontology
• Modeling of Language
• Chatbots

Uses of NLP in SEO:

In digital marketing, Search Engine Optimization (SEO) plays an important role in ranking a website of a company or business in the top SERPs (search engine result pages) by making it more visible to online visitors. You can use NLP for SEO in the following ways:

• NLP is responsible for utilizing and incorporating natural human language into the targeted keywords, which lets the search engines like Google recognize the applicability of the content by understanding what the online users are seeking.
• With the help of entity extraction algorithms, NLP helps produce entities from the website’s content which helps search engines understand the context or background of the website.
• It involves semantic searches by managing keywords, terms, and phrases that online users type to search for information.
• To improve search accuracy, NLP recognizes common usages of phrases, words, and synonyms.
• It is best for searching for long-tail or unpopular keywords, terms, and phrases in which users tend to enter all sorts of alternatives to what they are seeking.

NLU, on the other hand, is used for:

• Voice-driven assistants.
• Natural Language Search.
• Answering Questions.
• Web-scale relation extraction.
• Sentiment Analysis for Automated Trading.
• Legal Discovery.
• Business Intelligence.
• Social Media Analytics.
• Content Summarization.

The common uses of NLP and NLU are:

• Recognizing images and videos.
• Providing personal assistance.
• Moderation of content.

NLP vs. NLU: Difference between them?

Though the terms NLP and NLU sound almost similar and are often used interchangeably, there are a lot of differences between them, making them have their own distinct existence as separate branches in the field of artificial intelligence.

The key differences between NLU and NLP are listed below:

• NLU is a narrow concept, while NLP is a wider concept.
• NLU is only capable of understanding texts and speeches, while NLP is capable of making decisions based on those texts and speeches.
• NLU is focused mainly on the meaning of the sentences, while NLP is more focused on developing algorithms.
• NLU is a part of NLP, whereas NLP is a combination of NLU and something called “natural language generation” or NLG (Note: NLG is discussed in the very next heading).
• NLU reads and converts the given data into structured data, while NLP converts the unstructured data into structured data.
• While both NLU and NLP are using supervised learning and training their systems on using labeled data, there is a difference in how they handle the data.

In NLU, the texts and speech don’t need to be the same, as NLU can easily understand and confirm the meaning and motive behind each data point and correct them if there is an error.

Whereas in NLP, it totally depends on how the machine is able to process the targeted spoken or written data and then take proper decisions and actions on how to deal with them. Due to this, NLP does not always require the need for NLU.

NLP vs. NLU vs. NLG: The Future of Natural Language

With more progress in technology made in recent years, there has also emerged a new branch of artificial intelligence, other than NLP and NLU. It is another subfield of NLP called NLG, or Natural Language Generation, which has received a lot of prominence and recognition in recent times.

Machines programmed with NGL help in generating new texts in addition to the already processed natural language. They are so advanced and innovative that they appear as if a real human being has written them.

Here, it is also important to note that it is only through the combination of Natural Language Generation (NLG) and Natural Language Understanding (NLU) can result in the functioning of Natural Language Processing (NLP).

NLG + NLU = NLP

In a nutshell, it can be said that:

• NLU is in charge of reading and interpreting the grammar and context of natural language data, as well as determining the motivation behind it.
• NLP transforms this input data into structured data.
• It is then that NGL composes and generates new innovative human language texts based on this structured data.

Conclusion

As we summarize everything written under this NLU vs. NLP article, it can be concluded that both terms, NLP and NLU, are interconnected and extremely important for enhancing natural language in artificial intelligence.

Together with NLG, they will be able to easily help in dealing and interacting with human customers and carry out various other natural language-related operations in companies and businesses.