When we talk of natural language processing examples, rest assured there are plenty. But to better understand the examples, it is imperative first to understand what is NLP and how does it work?

However, before proceeding to the real-world examples of NLP, let’s look at how NLP fares as an emerging technology in terms of stats.

According to Statista, the NLP market is projected to grow almost 14 times larger by 2025 compared to its market size in 2017. It is equivalent to a boost from around 3 billion USD in 2017 to more than 43 billion in 2025. 

What is NLP and How Does It Work?

Branched out of artificial intelligence (AI), natural language processing (NLP) works on communication between humans and machines. It primarily focuses on how can a computer be programmed to understand, process and generate language like a human.

While the term was coined originally to refer to a system’s ability to read, it now encompasses all computational linguistics.
Subcategories include NLG (Natural Language Generation) and NLU (Natural Language Understanding).

NLG pertains to a computer’s ability to create its own communication, whereas NLU is about a system’s ability to understand the jargon, mispronunciations, misspellings, and other language variants.

Natural language processing works through machine learning (ML or machine learning). Machine learning systems store words and information in the different ways they are put together like any other form of data.

Words, phrases, sentences, and sometimes entire books are fed into the ML engines, where they are processed based on grammar rules, people’s real-life language habits, or both. The computer uses this data to find patterns and anticipate what comes next.

What Problems Can NLP Solve?

Its central idea is to give machines the ability to read and understand the languages ​​that humans speak. Natural Language Processing research aims to answer how people can understand the meaning of an oral/written sentence and how people understand what happened, when and where it happened, and the differences between an assumption, a belief, or a fact.

The elements common to any standard NLP architecture are:

1. Phonetic Level: Here, attention is paid to phonetics, the way we pronounce words. This level is essential when processing the spoken word, but not when working with written text.

2. Morphological Level: Here, we are interested in performing a morphological analysis of the discourse; study the structure of words to delimit and classify them.

3. Syntactic Level: A syntax analysis is performed here, which includes the action of dividing a sentence into each of its components.

4. Semantic Level: This level is a complement to the previous one. In semantic analysis, we seek to understand the meaning of the sentence. Words can have multiple meanings. The idea is to identify the appropriate meaning through the context of the sentence.

5. Discursive Level: The discursive level examines the sentence’s meaning in relation to another sentence in the text or paragraph of the same document.

6. Pragmatic Level: It involves analyzing sentences and how they are used in varying situations. It also assesses how the meanings of the same sentence change with respect to different situations.

All the levels discussed here do not work in isolation. Instead, they work in unison, complimenting each other. The goal of NLP systems and NLP applications is to get these definitions into a computer and then use them to form a structured, unambiguous sentence with a well-defined meaning.

22 Most Relatable Natural Language Processing Examples

Natural language processing is evolving rapidly, and so is the number of natural language processing applications in our daily lives. It’s good news for individuals and businesses, as NLP can dramatically affect how you manage your day-to-day activities.

It can speed up your processes, reduce your employees’ monotonous work, and even improve the relationship with your customers.
While the terms AI and NLP may conjure up notions of futuristic robots, there are already basic examples of NLP at work in our daily lives.

Here are some key examples.

     1. Text Analysis

Text analysis can be segmented into several subcategories, including morphological, grammatical, syntactic, and semantic.

Businesses can better organize their data and identify valuable templates and insights by analyzing text and highlighting different types of critical elements (such as topics, people, data, places, companies).

For example, e-commerce companies can conduct text analysis of their product reviews to see what customers like and dislike about their products and how customers use their products.

     2. Autocomplete

Autocomplete helps Google predict what you’re interested in based on the first few characters or words you enter.

When suggesting keywords relevant to you, Google relies on a wealth of data that catalogs what other consumers search for when entering specific search terms. The company uses NLP to understand this data and the subtleties between different search terms.

     3. Survey Analytics

In addition to analyzing reviews of their products, companies can also explore the results of their surveys to get actionable insights. Again, NLP helps these companies understand their raw data and generate these valuable insights.

Of course, smaller survey companies may choose to analyze their data manually to conclude what they need to. But if you have to search through a database with millions of records, it won’t be possible manually. It makes more sense here to automate the process using an NLP-equipped tool.

     4. Autocorrect

If you need typing on the go or your thumb size makes it difficult for you to hit the right keys on the keyboard, you will appreciate the convenience of the auto-correction.

Just like autocomplete, NLP technology sets the foundations of autocorrect applications of NLP. Here, NLP identifies the phrase closest to your typo and automatically changes your wrong expression to the correct one.

     5. Spell Check

In addition to autocompleting and auto-correction, there is also a spell checker. Students and professionals rely heavily on it.

Imagine having to submit a vital paper without being able to verify your work or sending an email to your organization’s CEO without checking the spelling.

Of course, you can check your text manually, but there is no doubt that spell check is more effective and convenient at detecting spelling or grammatical errors.

In addition to spell checking, NLP also backs other writing tools, such as Grammarly, WhiteSmoke, and ProWritingAid, to correct spelling and grammatical errors.

     6. Duplicate Detection

Many discussion forums and Q&A sites like Quora use duplicate detection technology to ensure seamless functioning.

For example, you get a whole bunch of similar questions in response to your query on Quora, like, “What’s healthier, fresh cherries or the frozen ones?”

Some responses may include:

Are fresh cherries good for health?
Are frozen cherries unhealthy?
What’s better, fresh cherries or frozen?
Should I buy fresh cherries or frozen?

Of course, it will take a lot of time and effort to post each question individually and go through the answers accordingly. On the other hand, getting all the related queries collated into a single thread makes things a lot easier.

That’s why sites like Quora resort to NLP in reducing duplicity in questions as much as possible. After a user ends typing their query on Quora, their NLP mechanics take over and analyze if it bears linguistic similarity to the other questions on the site.

Once identified, the site lends a list of similar questions so that the user gets all relevant queries in one place instead of posting questions individually.

     7. Smart Search

If you click on a search function on a website to find a specific query, the website will return the relevant results to find what you need. Simple, right? Well, yes, on the surface, but not so much what goes behind the scenes.

Let’s say you’re browsing through an e-commerce outlet that sells school leather goods, and the store has dozens of other listings showcasing leather goods. If you’re looking for “leather belts” and starting your search with “leather”, how does this store returns you with the belts at the top of the list and give you the most relevant listing?

Low and behold, it’s natural language processing in action yet again. NLP allows a store to capture context and add contextually relevant synonyms to search results. It helps the store predict what its customers are looking for and highlight relevant listings.

     8. Social Intelligence

Social intelligence is another one of the best natural language processing examples. Let’s see how.

If you’ve ever used a social media monitoring tool like Buffer or Hootsuite, NLP technology powers them. These tools allow you to check your social media channels to see if your brand is being cited and alert you when consumers talk about your brand.

Like many resellers and business owners alike, if negative reviews are spread on social media, they can ruin a brand’s reputation overnight.

With an understanding of these mechanics, companies must follow or listen to social media using these social intelligence tools and ensure an immediate resolution of potential crises.

     9. Sentiment Analysis

Many see sentiment analysis as social intelligence’s smaller subset, and quite rightly so.

Social intelligence is all about listening in on the social conversation and monitoring the social media landscape as a whole.

On the other hand, sentiment analysis focuses on identifying and determining whether or not the author of a post holds a negative, positive, or neutral opinion of a brand.

     10. Spam Filters

Do you think spam is not a big concern? Think again because the stats tell otherwise. According to research, spam accounts for almost half (45%) of all emails sent and approximately 14.5 billion unsolicited emails are sent every day.

Looking at the stats above, you might wonder how you don’t get more of it. That’s because we have good spam filters that mark suspicious emails as spam and prevent them from reaching our primary inbox.

How do these spam filters work? In addition to other factors (delivery, email domains, etc.), these filters use NLP technology to analyze email names and their content.

Vetting with these parameters makes it reasonably easy for spam filtration tools to assess what’s spam and what’s not. Emails filled with capitalized text and words like “free”, “buy now”, “promo”, etc., are most likely to be spam. So, they’re routed to our spam folders.

     11. Email Categorization

Likewise, if you are a Gmail user, there is an email classification that you are familiar with. If you look at your inbox in Gmail, you’ll see that your emails are segmented into three tabs: Primary, Social, and Promotion.

All your personal emails go to Primary, your messages from social media platforms go to Social and newsletters from the companies you subscribe to end up in Promotions.

To do so, Gmail counts on NLP to identify and evaluate the content of each email so that it can be accurately categorized.

However, let’s admit that this system is not 100% mature yet. So, you can find some newsletters (especially those that contain more text than images) filtered in the Primary tab.

     12. Chatbots

At the end of the “line,” there is a chat agent to help answer your question in the live chat. Have you ever wondered how chatbots understand what you ask and answer your questions?

Natural langue processing at your service again! Using NLP and machine learning, chatbots can better interpret consumer problems, recommend products, book appointments, and so much more.

(If the subject of chatbots intrigues you about its potential to improve your business, make sure to have a look at the Benefits of Using Chatbots for Business.)

     13. Next-Gen Chatbots

With standard chatbots becoming so ubiquitous, businesses want something special – the next-gen chatbots.

Take the Mastercard chatbot as an analogy. It was launched in 2016. This chatbot works as a virtual assistant and is almost as good as having a teller in your pocket.

Among other things, it can provide users with an overview of their high expenses, highlight unique benefits and promotions to which they are entitled, and much more.

Like regular chatbots, these updated bots also use NLP technology to understand user issues better.

     14. Translation Tools

If you’re traveling to a place where English (or your native language) isn’t usually spoken or understood, you’ll certainly want to install a translation app on your phone.

Google Translate enjoys unmatched popularity as a translation tool, used daily by 500 million people to understand more than 100 languages ​​worldwide.

It also resorts to NLP in understanding the terms or phrases that users are trying to translate, and the same is true for all other alternative translation applications.

     15. Bots + Knowledge Bases

Have you ever heard of self-service knowledge bases? These knowledge bases are primarily an online portal or library of information, including frequently asked questions, troubleshooting guides, etc.

By building a knowledge base, companies can empower their customers to solve their problems 24 hours a day, seven days a week, instead of contacting their support department and waiting for them to respond.

Since knowledge bases often comprise thousands of documents, it is in the company’s best interest to help its customers quickly identify suitable material. For this purpose, companies can link their chatbots to their knowledge base and configure their robots to send users helpful links to documents containing their desired information.

     16. Algorithmic Trading

People also know it as “Automated Trading” or “Black Box Trading”. It essentially involves using an algorithm to execute trading transactions.

Algorithmic trading takes place on two levels primarily. At the basic level, consumers can define guidelines (relevant to time, price and volume) that the program can use to execute a transaction. For instance, if you say you want to buy three lots of Tesla stock when the stock price drops to $1,500, the program can follow your instructions.

Algorithmic trading can also involve using robo-advisors to create portfolio optimization tips at a higher level. The program examines myriad data affecting financial markets (including the financial performance of companies, reports on mergers and acquisitions, etc.), providing tips on what an investor should buy or sell. NLP plays a vital role in helping such programs make sense of an unimaginable amount of data and information.

     17. Customer Service Automation

A company’s customer service costs a lot of time and money, especially when they’re growing. The key is to find ways to deal with automation.

Chatbots may be the first thing that comes to mind for all the right reasons. In reality, however, there are several other ways NLP can be used to automate customer service, for instance, detecting emotions and keywords in emails.

These emails may then receive automatic replies or be automatically assigned to the appropriate team. It means that customers’ emails don’t disappear into the air, and their concerns are catered to quickly.

Similarly, you can also automate the routing of support tickets to the right team. NLP is helpful in such scenarios by understanding what the customer needs based on the language they use. It is then combined with deep learning technology to ensure appropriate routing.

     18. Personalized Customer Experience (CX)

The digital age has made many aspects of our daily lives easier. As a result, consumers expect much more from interacting with their brand, especially when it comes to customization. Media organizations struggling to retain their subscriptions and readership have found this of interest, particularly choosing NLP as their savior.

Expert.ai’s NLP platform allows publishers and content producers to automate essential categorization and metadata information through tagging, creating readers’ more exciting and personalized experiences. The media can also have content tips so that users can see only the content that is most relevant to them.

     19. Smart Product Recommendations

Making it easier for customers to buy can help businesses yield much higher revenues. E-commerce businesses that keep visitors interested can drastically reduce segregation anxiety and encourage impulsive buying by recommending products that fit their needs.
In fact, a study found that product advice accounts for one-third of e-commerce revenue and improved cart delivery by 4.35%.
Amazon has historically stated that 35% of its revenue comes from purchases customers find through referrals.

Keywords have traditionally been the main focus of product advice, but today’s salespeople add context, data from previous research, and other factors to enrich the product range.

NLP’s statistics help traders make these combinations and get suitable recommendations.

     20. Improving Service Quality

User experience management is another excellent NLP application, both online and offline.

US retailer Nordstrom analyzed the amount of customer feedback collected through comments, surveys and thank you’s.

They found that many shoppers struggle to find their sales reps because they wear casual clothes instead of uniforms.

Nordstrom solved this by providing its reps with branded T-shirts in bright colors that customers can easily find.

Within two days of this pilot project, the company experienced a 30-point jump in crucial metrics they use to evaluate sales force effectiveness. A tiny observation can considerably impact business outcomes when new technologies like NLP step in.

     21. Smart Home Devices

Smart home devices like Alexa and Google Home are becoming more and more popular, especially among younger consumers. (Example? 58% of millennials say they currently have smart home devices with voice control.)

These smart home devices are great for multitasking. Suppose you want to play music but have your hands full preparing dinner. In that case, you can simply instruct Google Home to include your favorite playlist, and you’re done.

Here, your smart home device uses NLP to recognize your voice commands and take appropriate action. When giving a voice command to your smart assistant (like Google Assistant or Siri), NLP also works behind the scenes so that your assistant understands your instructions.

     22. Streamlining Patient Information

In most clinics, patients report their symptoms to a nurse or office, and the person records what they have shared with the doctor. Clinics and medical companies have now started using NLP to simplify patient information and automate the process of understanding patients’ conditions.

Now businesses have resources like 98point6 automated assistant, which uses NLP to allow patients to share their information. Before their appointment with the physician, a patient is simply required to text their medical history/conditions to the app. It would then streamline the information, passing it on to the physician.

Why NLP is the Future?

About 80% of the information surrounding us remains unstructured, which makes NLP one of the most eminent fields of data science with endless natural language processing uses. Countless researchers are dedicating their time and efforts daily to organize this data.

Natural language processing is a fascinating area that already offers many benefits to our daily lives. As technology evolves, we can expect more NLP applications in many industries.

NLP has transformed our ways of interacting with computers and will continue to do so in the future.

These artificial intelligence technologies will play a vital role in transforming from data-driven to intelligence-driven efforts as they shape and improve communication technologies in the coming years.

Now the choice lies in your hands. Be a part of the first wave of this transformation, or sit back to catch the crumbs left behind when all others around you move past you!