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Jan 3, 2023

NLP Vs NLU Vs NLG: A Comprehensive Guide For Content Marketer

Image of Saurabh Wadhawan, Co-founder
Saurabh Wadhawan
NLP Vs NLU Vs NLG: A Comprehensive Guide For Content Marketer

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AI technologies like NLP, NLU, and NLG let users use advanced computing to find the most relevant information for their search query.

NLP search algorithms are used by search engines like Google and Bing to index and understand the content on websites. They use the same technologies to understand what users are really looking for and match them with the most helpful content in their index. 

For them, it’s all about understanding what a searcher is looking for and providing the best sources of information on that topic. 

As a marketer trying to ace the search engine rankings game, it is important that you understand these technologies and their impact on SERPs. In this blog, we will help you understand what NLP, NLU, and NLG are and the difference between these terms. 

What is NLP?

Natural language processing is a method in computational linguistics that uses computer science, linguistic analysis, artificial intelligence, machine learning, and data science. It bridges the gap between humans and smart devices by enabling the latter to process natural-language textual data and produce helpful results. 

Whether you are talking to Alexa, Siri, or Ok Google, natural language search is rising in popularity on account of increased access to smart voice assistants. People across the world search using their native language. If you want to know more about natural language search, check out this Scalenut blog, ‘What Is Natural Language Searches And How Does It Work.’

The way natural language search works is that all of these voice assistants use NLP to convert unstructured data from our natural way of speaking into structured data that can be easily understood by machines. 

For instance, take the English word “running.” NLP helps computers understand that this word is an adjective of “run” and has a similar meaning. 

NLP programs help chatbots, voice assistants, and smart cars work by using complicated deep learning algorithms like hidden Markov chains, lemmatization, stemming, tokenization, entity recognition, part-of-speech tagging, and N-grams. 

Natural language processing is changing the way computers interact with people forever. It can do things like figure out which part of speech words and phrases belong to and make logical sequences of texts as a reply. 

How is NLP used in marketing?

As marketers, we are always on the lookout for new technology to create better, more focused marketing campaigns. NLP is one type of technology that helps marketing experts worldwide make their campaigns more effective. It enables us to move away from traditional marketing methods of "trial and error" and toward campaigns that are more targeted and have a higher return on investment. 

NLP can be used in several different ways to produce deep insights into the motivations of consumers. A thorough analysis of historical customer chats, for example, can reveal pain points that can then be used to create in-depth content marketing campaigns. 

Further, a SaaS platform can use NLP to create an intelligent chatbot that can understand the visitor’s questions and answer them appropriately, increasing the conversion rate of websites. 

Video ads, on the other hand, can use NLP to figure out what customers need, want, and feel about a product and make more effective video ads that connect with the target audience. 

That’s not all. NLP plays an important role in content marketing and SEO as well. NLP algorithms are used by search engines to figure out how good a piece of content is and how relevant it is to a user's search query. It is the latest disruptive technology in the SEO universe. 

In this in-depth Scalenut blog post, ‘NLP SEO: What Is It And How To Use It For Content Optimization,’ you can learn more about how NLP affects search engine optimization.

What is NLU? 

Natural language understanding is a subset technology of NLP that focuses on understanding human language. The way we speak is unstructured data. People can use different words or jargon to say the same thing in the same language. NLU helps computer programs understand the context, intent, semantics, and sentiment of human language by adapting our language into a computer-friendly data structure. 

Syntax analysis focuses on sentence structure to understand grammar and other aspects of an input text. The semantic analysis builds on that and zeros in on the meaning of the input data in the given context. And sentiment analysis helps them understand the overall emotional quotient in relationship with the entities mentioned in the content. 

For instance, if you ask your voice assistant, “How’s the weather?” NLU helps the machine understand that you are asking about the weather at your given location. Or if you ask, “How’s today?” it will help the assistant understand that you’re talking about the weather in that location. 

How is NLU used in marketing?

Being a subset of NLP, natural language understanding plays an important role in all the use cases of NLP in marketing. 

For example, NLU helps companies analyze chats with customers to learn more about how people feel about a product or service. Also, if you make a chatbot, NLU will be used to read visitor messages and figure out what their words and sentences mean in context. 

Or, if you have a lot of information from a market survey, you can use NLU to pull out statistical information and get a sense of what all the answers mean. Such insights can then be used to fine-tune your marketing strategy.

What is NLG? 

NLG systems are another subset of NLP that helps in text summarization and producing appropriate responses. The relationship between NLU and NLG is that with NLU, you understand what the visitor, user, or customer is asking for, and with NLG systems, you generate a response. 

When we ask our voice assistant, “How’s the weather?” it analyzes the meaning and collects information on the weather in our location, and replies with a few sentences about the weather. Natural language generation is the final stage in NLP programs that forms human-sounding sentences with the exact information we ask for. 

How is NLG used in marketing?

Marketers use NLG to program machines to generate human-sounding text in response to the result of the NLU processes. For example, if we are developing a voice assistant of our own, you would want it to speak, and that’s what NLG helps you achieve. 

Again, being a subset of NLP, natural language generation is used in almost every use-case of NLP. For example, when developing a chatbot, NLG helps us program the chatbot to reply in a specific way. If our chatbot is conversational and friendly, it might have an informal tone of voice with a friendly choice of words like “Hey!” instead of “Hello.”

Difference between NLP, NLU and NLG?

Although related, NLP, NLU, and NLG have a few key points of difference. The most important is the difference in the technology’s scope of work. NLU works with the input data, NLG works with the output data, and NLP encompasses both these aspects and focuses on the delivery of the results from NLU and NLG. 

When we talk about natural language processing, NLU and NLG play a crucial role in the process. NLU helps computers understand the text they are given and its nuances, and NLG helps them produce useful output. Together, they form NLP, an artificially intelligent computing system that understands humans and the nitty-gritty of human language. 

From search engines trying to understand search queries to chatbots talking like humans, NLU, NLP, and NLG are breakthroughs in technology that will change the way we interact with computers forever. 

Utilize NLP technology in your content marketing with Scalenut 

Scalenut is an all-in-one content marketing and SEO platform that enables you to use NLP, NLU, and NLG for creating content. All you have to do is enter your primary keyword and the location you are targeting. 

Scalenut will analyze the top-ranking content on the internet and produce a comprehensive research report. In this report, you will find a list of NLP keywords that your competitors are using, which you can use in your content to rank higher. 

Further, once you have created a content brief for your topic, you can use NLG features such as “write,” “instruct,” and AI templates to generate human-sounding text. You can also change the AI output settings, such as output length and creativity. 

Throughout the content creation process, Scalenut helps you gauge the quality of your content with the help of our proprietary content grade, which analyzes text based on the NLP terms and quality of the content. 

If your customers are using NLP to find information related to your products, creating a marketing plan around NLP terms makes sense. It helps your content get in front of the right audience with the right search intent.

Whether you are marketing your products through blogs or posts on social media, an understanding of NLP and its subsets combined with a tool like Scalenut is a sure-shot recipe for success. 

We hope this blog helps you understand the inner workings of an NLP-powered search engine. To know more about the impact of NLP on SEO, refer to this in-depth Scalenut blog on 12 real-world examples of Natural Language Processing (NLP).

About Scalenut 

Scalenut is an all-in-one SEO and content marketing platform that is powered by AI and enables marketers all over the world to make high-quality, competitive content at scale. From research, planning, and outlines to ensuring quality, Scalenut helps you achieve the best in everything. 

Empower your content marketing campaigns with AI. Sign up on Scalenut, and start creating content today.

Saurabh Wadhawan
Co-founder
ABout the AUTHOR
Saurabh Wadhawan
Co-founder

Saurabh is the Co-Founder of Scalenut. He is a technology veteran with over a decade of experinece in product development. He is the co-captain of the ship, steering product strategy, development, and management at Scalenut. His goal is to build a platform that can be used by organizations of all sizes and domains across borders.

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