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6 Real-World Examples of AI Natural Language Processing

Expert.ai Team - 25 August 2023

Natural language processing examples and apps

Language is an essential part of our most basic interactions. The same has also become true of technology. At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans.

With the recent focus on large language models (LLMs), AI technology in the language domain, which includes NLP, is now benefiting similarly. You may not realize it, but there are countless real-world examples of NLP techniques that impact our everyday lives. Here are some of the most common applications.

Email Filters

If you’ve used an email account at any time in the last 10 years, you’ve undoubtedly benefited from NLP technology. Once an algorithm is sufficiently trained on email textual data, it can successfully identify, categorize and label emails as regular emails, spam emails or malicious emails—the latter of which is usually removed from your account before you even see it. Similarly, other email filters may be used, such as for social or promotional emails, depending on the email provider.

Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data.

Language Translation

For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text.

Now, thanks to AI and NLP, algorithms can be trained on text in different languages, making it possible to produce the equivalent meaning in another language. This technology even extends to languages like Russian and Chinese, which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters.

Search Results

Online search is now the primary way that people access information. Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise.

However, enterprise data presents some unique challenges for search. Varied repositories that create data silos are one problem. The data itself is another. The information that populates an average Google search results page has been labeled—this helps make it findable by search engines. However, the text documents, reports, PDFs and intranet pages that make up enterprise content are unstructured data, and, importantly, not labeled. This makes it difficult, if not impossible, for the information to be retrieved by search.

Even when retrieved by search, accuracy matters. Too many results of little relevance is almost as unhelpful as no results at all. As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. To be useful, results must be meaningful, relevant and contextualized.

By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response.

The recent spotlight on large language models and the popularity of the question and answer capability of ChatGPT have raised the question: will such applications change or replace today’s traditional search? In a word, no.

First, the capability of interacting with an AI using human language—the way we would naturally speak or write—isn’t new. Smart assistants and chatbots have been around for years (more on this below). And while applications like ChatGPT are built for interaction and text generation, their very nature as an LLM-based app imposes some serious limitations in their ability to ensure accurate, sourced information. Where a search engine returns results that are sourced and verifiable, ChatGPT does not cite sources and may even return information that is made up—i.e., hallucinations.

Smart Assistants

Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices.

These applications actually use a variety of AI technologies. Here, NLP breaks language down into parts of speech, word stems and other linguistic features. Natural language understanding (NLU) allows machines to understand language, and natural language generation (NLG) gives machines the ability to “speak.”Ideally, this provides the desired response.

Compared to chatbots, smart assistants in their current form are more task- and command-oriented.


A chatbot system uses AI technology to engage with a user in natural language—the way a person would communicate if speaking or writing—via messaging applications, websites or mobile apps. The goal of a chatbot is to provide users with the information they need, when they need it, while reducing the need for live, human intervention.

How do chatbots work? Artificial intelligence and NLP technologies allow systems to understand a user’s request in their own words in an engaging, conversational dialogue. NLP-powered chatbots, which can be thought of as another form of smart assistant, respond to textual input from users. As with other AI-based applications, how the system understands the user’s request depends on the AI approach at work. This could include:

  • a standard logic tree, where users choose from a selection of prompts
  • a keyword recognition approach where corresponding content is returned according to the keyword identified
  • a machine learning approach that uses large data sets to train the system
  • a symbolic AI approach that uses natural language technology to “understand” the intent and context of the query
  • a hybrid approach that leverages multiple technologies

For many businesses, the chatbot is a primary communication channel on the company website or app. It’s a way to provide always-on customer support, especially for frequently asked questions.

Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few. In contrast to the NLP-based chatbots we might find on a customer support page, these models are generative AI applications that take a request and call back to the vast training data in the LLM they were trained on to provide a response. It’s important to understand that the content produced is not based on a human-like understanding of what was written, but a prediction of the words that might come next. That’s why applications like ChatGPT are more suited for more creative tasks, such as generating a first draft, writing code or summarizing text, rather than tasks that rely on a specific domain or knowledge base, as many customer service oriented chatbots do.

Personalized CX

The Digital Age has made many aspects of our day-to-day lives more convenient. As a result, consumers expect far more from their brand interactions — especially when it comes to personalization.

Today, the availability of ever more data, advanced analytics and even more advanced technologies means that companies have more information available than ever before with which to build a connection with customers. Furthermore, it’s what customers expect of the companies they do business with: personalization is now the standard for engagement. A McKinsey survey found that “71% of consumers expect companies to deliver personalized interactions.”

Media organizations, which have struggled to retain readership and subscribers, have taken note and are turning to natural language processing for relief.

Expert.ai’s NLP platform gives publishers and content producers the power to automate important categorization and metadata information through the use of tagging, creating a more engaging and personalized experience for readers. Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them.

Text Analytics

Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights. As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features, such as named entity extraction, give users the power to identify key elements like names, dates, currency values, and even phone numbers in text.

Then, the entities are categorized according to predefined classifications so this important information can quickly and easily be found in documents of all sizes and formats, including files, spreadsheets, web pages and social text. The use of NLP in the insurance industry allows companies to leverage text analytics and NLP for informed decision-making for critical claims and risk management processes.


Natural language processing is the key that unlocks the potential of AI to comprehend and utilize unstructured language data, bridging the automation gap between humans and technology and leveraging existing assets for new insights that were previously unavailable. As digital transformation continues to drive the need to process unstructured data across every industry, artificial intelligence solutions incorporating NLP are becoming business-critical for their ability to improve efficiency, reduce risk and costs and help companies create new opportunities.

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