Best Natural Language Processing Examples in 2022

This article will cover some of the common Natural Language Processing examples in the industry today and its definition along with uses.
Natural language processing examples

In today’s IT centred business environment, companies receive almost 95% of their customer data in the form of unstructured text. Sources include emails, surveys, online reviews, social media posts and comments on different forums.

Natural Language Processing (NLP), Cognitive services and AI an increasingly popular topic in business and, at this point, seems all but necessary for successful companies. NLP holds power to automate support, analyse feedback and enhance customer experiences. Although implementing AI technology might sound intimidating, NLP is a relatively pure form of AI to understand and implement and can propel your business significantly. This article will cover some of the common Natural Language Processing examples in the industry today.

Converse Smartly® is an advanced speech recognition application for the web developed by Folio3. It is a strong contender in the use and application of Machine Learning, Artificial Intelligence and NLP. It enables organisations to work smarter, faster and with greater accuracy. The advanced features of the app can analyse speech from dialogue, team meetings, interviews, conferences and more.

Natural Language Processing Definition, and What Is it?

In dictionary terms, Natural Language Processing (NLP) is “the application of computational techniques to the analysis and synthesis of natural language and speech”. What this jargon means is that NLP uses machine learning and artificial intelligence to analyse text using contextual cues. In doing so, the algorithm can identify, differentiate between and hence categorise words and phrases and therefore develop an appropriate response. Some of the most common NLP examples include Spell Check, Autocomplete, Voice-to-Text services as well as the automatic replies system offered by Gmail.

Natural Language Processing Uses in Businesses

Given that communication with the customer is the foundation upon which most companies thrive, communicating effectively and efficiently is critical. Regardless of whether it is a traditional, physical brick-and-mortar setup or an online, digital marketing agency, the company needs to communicate with the customer before, during and after a sale. The use of NLP, in this regard, is focused on automating the tracking, facilitating, and analysis of thousands of daily customer interactions to improve service delivery and customer satisfaction.

Improve user experience

A website integrated with NLP can provide more user-friendly interactions with the customer. Features such as spell check, autocorrect/correct make it easier for users to search through the website, especially if they are unclear of what they want. Most people search using general terms or part-phrases based on what they can remember. Enabling visitor in their search stops them from navigating away from the page in favour of the competition.

Automate support

Providing adequate support can be tedious and labour intensive. To improve communication efficiency, companies often have to either outsource to 3rd-party service providers or use large in-house teams. AI without NLP, cannot cope with the dynamic nature of human interaction on its own. With NLP, live agents become unnecessary as the primary Point of Contact (POC). Chatbots can effectively help users navigate to support articles, order products and services, or even manage their accounts.

Monitor and Analyse Feedback

Feedback comes in from many different channels with the highest volume in social media and then reviews, forms and support pages, among others. NLP can aggregate and help make sense of all the incoming information from feedback, and transform it into actionable insight.

Improve Internal Communication

One of the most monotonous and time-consuming aspects of any internal communication is record keeping. Minutes and transcriptions can take hours, but with NLP, interviews, meetings, seminars, conferences can all be converted to text quickly.

Make Sense of Unstructured data

There are a large number of information sources that form naturally in doing business. These can sometimes overwhelm human resources in converting it to data, analyzing it and then inferring meaning from it. NLP automates the process of coding, sorting and sifting of this text and transforming it to quantitative data which can be used to make insightful decisions.

Folio3’s NLP and Cognitive Services

Folio3 is a California based company that offers robust cognitive services through its NLP services and applications built using superior algorithms. The company provides tailored machine learning applications that enable extraction of the best value from your data with easy-to-use solutions geared towards analysing sophisticated text and speech. Their NLP apps can process unstructured data using both linguistic and statistical algorithms.

Natural Language Processing Examples in 2021

Below are some of the common real-world Natural Language Processing Examples. Most of these examples are ways in which NLP is useful is in business situations, but some are about IT companies that offer exceptional NLP services.

1) Search Autocorrect

Making mistakes when typing, AKA’ typos‘ are easy to make and often tricky to spot, especially when in a hurry. If the website visitor is unaware that they are mistyping keywords, and the search engine does not prompt corrections, the search is likely to return null. In which case, the potential customer may very well switch to a competitor. Therefore, companies like HubSpot reduce the chances of this happening by equipping their search engine with an autocorrect feature. The system automatically catches errors and alerts the user much like Google search bars.

2) Search Autocomplete

Autocomplete services in online search help users by suggesting the rest of the keywords after entering a few or a partial word. Historical data for time, location and search history, among other things becoming the basis. Autocomplete features have no become commonplace due to the efforts of Google and other reliable search engines.

Salesforce is an example of a software that offers this autocomplete feature in their search engine. As mentioned earlier, people wanting to know more about salesforce may not remember the exact phrase and only just a part of it.

3) Form Spell Check

Frequent flyers of the internet are well aware of one the purest forms of NLP, spell check. It is a simple, easy-to-use tool for improving the coherence of text and speech. Nobody has the time nor the linguistic know-how to compose a perfect sentence during a conversation between customer and sales agent or help desk. Grammarly provides excellent services in this department, even going as far to suggest better vocabulary and sentence structure depending on your preferences while you browse the web.

4) Smart Search

A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results. Klevu is a company that provides smart search capability powered by NLP coupled with self-learning technology. Best suited for e-commerce portals, Klevu offers relevant search results and personalised search based on historical data on how a customer previously interacted with a product or service.

5) Messenger or chatbots

Many companies today use messenger apps coupled with social media, to deliver connect and interact with customers. Facebook Messenger is one of the more recent platforms used for this purpose. In this case, NLP enables expansion in the use of automatic reply systems so that they not only advertise a product or service but can also fully interact with customers. The more comfortable the service is, the more people are likely to use the app. Uber took advantage of this concept and developed a Facebook Messenger chatbot, thereby creating a new source of revenue for themselves.

6) Machine Translation

Translation of both text and speech is a must in today’s global economy. Regardless of the physical location of a company, customers can place orders from anywhere at any time. The trouble lies in the apparent language barrier. When communicating with customers and potential buyers from various countries. Lilt is a translation tool that seeks to make the process easier. It integrates with any third-party platform to make communication across language barriers smoother and cheaper than human translators.

7) Virtual Assistants

Mastercard launched its first chatbot in 2016 which was compatible with Facebook Messenger. Although, compared to Uber’s bot, this bot functions more like a virtual assistant. Having a bank teller in your pocket is the closest you can come to the experience of using the Mastercard bot. The assistant can complete several tasks and offers helpful information such as a dashboard of spending habits and alerts for new benefits and offers available.

8) Knowledge Base Support

An answer bot provides direction within a pre-existing knowledge base. For example, Zendesk offers answer bot software for businesses that uses NLP to answer the questions of potential buyers’. The bot points them in the right direction, i.e. articles that best answer their questions. If the answer bot is unsuccessful in providing support, it will generate a support ticket for the user to get them connected with a live agent.

9) Email filters

Email filters were one of the earliest applications of NLP online. It began with just spam filters based on previous interactions with certain types of emails by the mail clients user base. By uncovering certain words or phrases that signal a spam message the mail client immediately flags the email and moves it to spam. One of the more recent additions to NLP applications in email Gmail’s classification system. The system recognises if emails belong in one of three categories (primary, social, or promotions) based on their contents. For all Gmail users, this keeps your inbox to a manageable size with meaningful, relevant emails you wish to review and respond to quickly.

10) Survey Analytics

One of the best ways for NLP to improve insight and company experience is by analysing data for keyword frequency and trends, which tend to indicate overall customer sentiment about a brand. Even though the name, IBM SPSS Text Analytics for Surveys is one of the best software out there for analysing almost any free text, not just surveys. One reviewer tested the system by using his Twitter archive as an input. Although the user interface leaves something to be desired.

11) Social Media Monitoring

Monitoring and evaluation of what customers are saying about a brand on social media can help businesses decide whether to make changes in brand or continue as it is. NLP makes this process automatic, quicker and more accurate. Social media listening tool such as Sprout Social help monitor, evaluate and analyse social media activity concerning a particular brand. The services sports a user-friendly interface does not require a ton of input for it to run.

12) Marketing Strategy

Developing the right content marketing strategies is an excellent way to grow the business. MarketMuse is one such company that produces marketing content strategy tools powered by NLP and AI. Much like Grammarly, the software analyses text as it is written, thereby giving detailed instructions about the direction to ensure that the content of the highest quality. MarketMuse also analyses current affairs and recent news stories, thus providing users to create relevant content quickly.

13) Descriptive Analytics

Reviews increase the confidence in potential buyers for the product or service they wish to procure. Collecting reviews for products and services has many benefits and can be used to activate seller ratings on Google Ads. However, NLP-equipped tools such as Wonderflow’s Wonderboard can bring together customer feedback, analyse it and show the frequency of individual advantages and disadvantage mentions.

14) Natural Language Programming

Programming is a highly technical field which is practically gibberish to the average consumer. NLP can help bridge the gap between the programming language and natural language used by humans. In this way, the end-user can type out the recommended changes, and the computer system can read it, analyse it and make the appropriate changes.

15) Automatic Insights

Automatic insights are the next step in NLP applications. This feature does not merely analyse or identify patterns in a collection of free text but can also deliver insights about a product or service performance that mimics human speech. In other words, let us say someone has a question like “what is the most significant drawback of using freeware?”. In this case, the software will deliver an appropriate response based on data about how others have replied to a similar question.

16) Monitor and Analyse Feedback

Feedback comes in from many different channels with the highest volume in social media and then reviews, forms and support pages, among others. NLP can aggregate and help make sense of all the incoming information from feedback, and transform it into actionable insight.


As is evident from the long list of Natural Language Processing examples described above, there is an infinite number of possibilities for NLP application in business. Teaching AI to read, listen and speak as humans will lead to significant efficiency improvements in businesses operations. From sifting through incoming emails to generating automatic insight using computer vision, NLP can change the way people interact with technology altogether.

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