Jan 10, 2023

12 Real-World Examples Of Natural Language Processing (NLP) In Action

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Aditya Singh Rajput
12 Real-World Examples Of Natural Language Processing (NLP) In Action

12 Real-World Examples Of Natural Language Processing (NLP) In Action

Wondering how NLP helps organizations become more efficient? Check out 12 real-world examples of natural language processing in action here.
12 Real-World Examples Of Natural Language Processing (NLP) In Action

Table of contents

One of the biggest proponents of NLP and its applications in our lives is its use in search engine algorithms. Google uses natural language processing (NLP) to understand common spelling mistakes and give relevant search results, even if the spellings are wrong. 

Through this blog, we will help you understand the basics of NLP with the help of some real-world NLP application examples. 

What is natural language processing?

Natural language processing is an AI technology that enables computers to understand human language and its delicate ways of communicating information. 

Natural language processing is more than a fancy technology. It is a way of modern life, something that all of us use, knowingly or unknowingly. 

Has the following ever happened to you?

You mistype a word in a Google search, but it gives you the right search results anyway. 

If yes, then you have already seen NLP in action.

If this hasn't happened, go ahead and search for something on Google, but only misspell one word in your search. 

Let’s search for “lawn mower,” but spell it “lan mowr.”

As you can see, Google recognized our spelling mistake in “lan mowr” and gave us a SERP for lawn mower. 

It’s not just correcting our spelling mistakes; search engines and voice assistants like Google’s “Ok Google,” Apple’s “Siri,” and “Alexa” from Amazon use NLP algorithms. They enable people to search in their natural language and still find the most relevant results.

To know more about NLP and the rise of natural language searches, check out this detailed Scalenut blog, ‘What Is Natural Language Searches And How Does It Work.’

As marketers, you can use NLP tools to enhance the quality of your content. By identifying NLP terms that searchers use, marketers can rank better on NLP-powered search engines and reach their target audience. 

With NLP-based chatbots on your website, you can better understand what your visitors are saying and adapt your website to address their pain points. Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets. 

Natural language processing examples every business should know 

Now that you have a fair understanding of NLP and how marketers can use it to enhance the effectiveness of their efforts, let’s look at some NLP examples to inspire you. 

  1. Search Engine Results 

If you go to your favorite search engine and start typing, almost instantly, you will see a drop-down list of suggestions. 

This is what Google shows when we type “FIFA World Cup.”

This is what Bing shows when we type “FIFA World Cup.”

Now, chances are very high that if you were trying to search for “FIFA World Cup,” you were going to look for upcoming matches. 

This amazing ability of search engines to offer suggestions and save us the effort of typing in the entire thing or term on our mind is because of NLP. 

Now, if you were to just search for “FIFA World Cup” on Google, it would show you something like this:

Okay, let’s change it up a bit. 

Let’s search for “how to build a treehouse.”

As you can see, Google tries to directly answer our searches with relevant information right on the SERPs.

Whether you search for “FIFA World Cup” or “how to build a treehouse,” Google gives you the most relevant information, i.e., “FIFA world cup schedule” and “steps to build a treehouse.”

Isn’t it amazing how it almost always understands what we want even though we do not type the entire search query?

This is because of NLP. 

It is through NLP’s computational linguistics methods like text classification and user intent analysis that search engines like Google produce “freakishly” relevant results. 

  1. Predictive text and autocorrect

NLP does not just help search engines. It is also used by various applications for predictive text analysis and autocorrect. If you have used Microsoft Word or Google Docs, you have seen how autocorrect instantly changes the spelling of words. 

Screenshot of Google docs autocorrect feature

Similar to spelling autocorrect, Gmail uses predictive text NLP algorithms to autocomplete the words you want to type. 

As you can see, Gmail predicted the word “works” automatically. Such features are the result of NLP algorithms working in the background.  

And it’s not just predictive text or auto-correcting spelling mistakes; today, NLP-powered AI writers like Scalenut can produce entire paragraphs of meaningful text. Users simply have to give a topic and some context about the kind of content they want, and Scalenut creates high-quality content in a few seconds. 

  1. Email filters

Email filters are a major use-case of NLP. At first, it was just analyzing inboxes and filtering malicious or spam emails. But this feature has come a long way and evolved into categorizing emails based on the type of content they carry inside. 

Today, Gmail automatically categorizes the emails in your inbox as social emails, update emails, forums, promotions, and spam with filters. It is able to do this with the help of NLP algorithms that help analyze the content of emails. 

  1. Text analytics 

As internet users, we share and connect with people and organizations online. We produce a lot of data—a social media post here, an interaction with a website chatbot there. 

Wouldn’t it be great if, as an organization, you could analyze all of this data and gain useful insights about your products and services? 

NLP-based text analysis can help you leverage every “bit” of data your organization collects and derive insights and information as and when required. 

For instance, through optical character recognition (OCR), you can convert all the different types of files, such as images, PDFs, and PPTs, into editable and searchable data. It can help you sort all the unstructured data into an accessible, structured format. 

Another use of NLP in text analysis is linguistic computation. If your website has a chatbot, you can use NLP algorithms to record all the customer interactions and answer questions such as “What does a visitor ask the most?” “Is it product- or service-related?” and “What can we do to address this and improve the customer experience?”

As much as 80% of an organization's data is unstructured, and NLP gives decision-makers an option to convert that into structured data that gives actionable insights. 

Organizations in any field, such as SaaS or eCommerce, can use NLP to find consumer insights from data. 

  1. Language translation 

One of the most helpful applications of NLP is language translation. You can see this for yourself. Just visit the Google Translate website and select your language and the language you want to translate your sentences into. 

As you start typing, Google will start translating every word you say into the selected language. Above, you can see how it translated our English sentence into Persian. 

The amazing thing is that it is not limited to typing text; you can also use Google Voice Translator. Again, it is NLP working in the background to match English words with Persian synonyms and check for Persian grammar while translating. 

  1. Text summarization 

Another useful application of NLP is text summarization. With the help of NLP, computers can easily understand human language, analyze content, and make summaries of your data without losing the primary meaning of the longer version. 

For instance, in the “tree-house” example above, Google tries to sort through all the “tree-house” related content on the internet and produce a relevant answer right there on the search results page. This is NLP-powered text summarization in action. 

And it’s not just search engines. Marketers use AI writers that employ NLP text summarization techniques to generate competitive, insightful, and engaging content on topics.

Whether it's built-in software or a popular tool like Scalenut, users can choose topics, and NLP algorithms will look at the information available on that topic and generate text that is relevant to that topic. 

Let’s ask Scalenut, “How did David beat Goliath?”

As you can see, our AI writer knows how David defeated Goliath. David used what he knew and made detailed plans to win the epic battle. This is similar to how marketers compete with industry giants through content marketing strategies. 

The point here is that by using NLP text summarization techniques, marketers can create and publish content that matches the NLP search intent that search engines detect while providing search results. 

If you are using most of the NLP terms that search engines look for while serving a list of the most relevant web pages for users, your website is bound to be featured on the search engine right beside the industry giants.

  1. Sentiment analysis for brand building 

Sentiment analysis is a big step forward in artificial intelligence and the main reason why NLP has become so popular. By analyzing data, NLP algorithms can predict the general sentiment expressed toward a brand. 

As you can see in the above example, sentiment analysis of the given text data results in an overall entity sentiment score of +3.2, which can be translated into layman’s terms as "moderately positive" for the brand in question.

NLP is used in consumer sentiment research to help companies improve their products and services or create new ones so that their customers are as happy as possible. There are many social listening tools like “Answer The Public” that provide competitive marketing intelligence. 

NLP sentiment analysis helps marketers understand the most popular topics around their products and services and create effective strategies. 

  1. Smart assistants 

All of us have used smart assistants like Google, Alexa, or Siri. Whether it is to play our favorite song or search for the latest facts, these smart assistants are powered by NLP code to help them understand spoken language.

Using speech-to-text translation and natural language understanding (NLU), they understand what we are saying. Then, using text-to-speech translations with natural language generation (NLG) algorithms, they reply with the most relevant information. 

If you want to know more about the relationship between NLP, NLU, and NLG, here is a Scalenut blog on NLP vs. NLU vs. NLG: What’s the difference?

  1. AI powered chatbots and virtual assistants 

The central purpose of customer service is to solve problems. If users are unable to do something, the goal is to help them do it. This usually involves a specific set of steps to be followed. AI-powered chatbots can help customers with such problems. 

Customer chatbots work on real-life customer interactions without human intervention after being trained with a predefined set of instructions and specific solutions to common problems. 

And it’s not just customer-facing interactions; large-scale organizations can use NLP chatbots for other purposes, such as an internal wiki for procedures or an HR chatbot for onboarding employees. 

For example, the Loreal Group used an AI chatbot called Mya to increase the efficiency of its recruitment process. 

They use this chatbot to screen more than 1 million applications every year. The chatbot asks candidates for basic information, like their professional qualifications and work experience, and then connects those who meet the requirements with the recruiters in their area. 

AI-powered chatbots and virtual assistants are increasing the efficiency of professionals across departments. Chatbots and virtual assistants are made possible by advanced NLP algorithms. They give customers, employees, and business partners a new way to improve the efficiency and effectiveness of processes. 

  1. Automating customer support tasks

NLP-based chatbots are also efficient enough to automate certain tasks for better customer support. For example, banks use chatbots to help customers with common tasks like blocking or ordering a new debit or credit card. 

All you have to do is type or speak about the issue you are facing, and these NLP chatbots will generate reports, request an address change, or request doorstep services on your behalf. 

NLP is eliminating manual customer support procedures and automating the entire process. It enables customers to solve basic problems without the need for a customer support executive. 

With NLP-powered customer support chatbots, organizations have more bandwidth to focus on future product development. 

  1. Insider threat detection and cybersecurity

In today’s age, information is everything, and organizations are leveraging NLP to protect the information they have. Internal data breaches account for over 75% of all security breach incidents

As organizations grow, they are more vulnerable to security breaches. With more and more consumer data being collected for market research, it is more important than ever for businesses to keep their data safe. 

It could be sensitive financial information about customers or your company's intellectual property. Internal security breaches can cause heavy damage to the reputation of your business. The average cost of an internal security breach in 2018 was $8.6 million. This number would have only gone up in 2022. 

But not to worry. NLP has the solution. 

Businesses can avoid losses and damage to their reputation that is hard to fix if they have a comprehensive threat detection system. NLP algorithms can provide a 360-degree view of organizational data in real-time. 

Enterprise communication channels and data storage solutions that use natural language processing (NLP) help keep a real-time scan of all the information for malware and high-risk employee behavior. 

For example, suppose an employee tries to copy confidential information somewhere outside the company. In that case, these systems will not allow the device to make a copy and will alert the administrator to stop this security breach.

In addition to monitoring, an NLP data system can automatically classify new documents and set up user access based on systems that have already been set up for user access and document classification. 

  1. Content analysis and grading

One of the most interesting applications of NLP is in the field of content marketing. AI-powered content marketing and SEO platforms like Scalenut help marketers create high-quality content on the back of NLP techniques like named entity recognition, semantics, syntax, and big-data analysis. 

The way this works is that NLP algorithms analyze the top-ranking pages on the internet for a given keyword and a target location. They help marketers find important NLP terms that search engines using NLP, like Google, consider very relevant to the keyword.

Also, tools like Scalenut help content creators judge the quality of their work with a proprietary content grade that checks for NLP terms and helps during the content creation process. They allow you to put your best foot forward and publish the most engaging content for your blogs, landing pages, emails, and customer questions. 

There are numerous ways NLP influences search engine optimization; learn about them all in this in-depth Scalenut blog, ‘NLP SEO: What Is It And How To Use It For Content Optimization.’

Explore NLP for content marketing with Scalenut 

Scalenut is an NLP-based content marketing and SEO tool that helps marketers from every industry create attractive, engaging, and delightful content for their customers. 

Discover how AI technologies like NLP can help you scale your online business with the right choice of words and adopt NLP applications in real life. 

Sign up for a free trial today.

Aditya Singh Rajput
Content Writer
ABout the AUTHOR
Aditya Singh Rajput
Content Writer

Aditya is a professional content writer at Scalenut. He is passionate about AI and its applications in demystifying the world of content marketing and SEO for marketers. He is on a mission to bridge the content gap between organic marketing topics on the internet and help marketers get the most out of their content marketing efforts.

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