14 Natural Language Processing Examples NLP Examples

6 Real-World Examples of Natural Language Processing

nlp engines examples

We may also create complex data structures or objects with annotations (standardised intents). This is a deep neural network that represents various text strings in the form of semantic vectors. We can use the distance metric (here – cosine) as an activation function to propagate similarity. Next, the trained model can efficiently reproduce questions the same way as paragraphs and documents in one space. For example, for a model that was trained on a news dataset, some medical vocabulary can be considered as rare words.

nlp engines examples

Search engines use natural language processing to throw up relevant results based on the perceived intent of the user, or similar searches conducted in the past. Combining AI, machine learning and natural language processing, Covera Health is on a mission to raise the quality of healthcare with its clinical intelligence platform. The company’s platform links to the rest of an organization’s infrastructure, streamlining operations and patient care.

Learn how to integrate a pretrained LLM with your database to build a chatbot for efficient domain-specific query responses.

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. It is a way of modern life, something that all of us use, knowingly or unknowingly. Through this blog, we will help you understand the basics of NLP with the help of some real-world NLP application examples.

From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging. This stage is necessary so that the development team can comprehend our client’s requirements. A team must conduct a discovery phase, examine the competitive market, define the essential features for your future chatbot, and then construct the business logic of your future product. As a result, your chatbot must be able to identify the user’s intent from their messages. AI chatbots understand different tense and conjugation of the verbs through the tenses. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility.

How to create an NLP chatbot

More recently, the popular web platform Gmail has been using NLP to classify messages into promotion, Social, or important categories. Again, keywords and phrases in the message text form the basis of comparison enabling natural language processing algorithms to sort through incoming mail. Natural language processing mechanisms and tools make it possible for machines to sift through information and reroute it with little or no human intervention, allowing for the real-time automation of various processes. And by adapting them to the specific characteristics of a given sub-language or technical vocabulary, NLP tools can be custom-tailored to the needs of virtually any industry.

nlp engines examples

Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment. A major benefit of chatbots is that they can provide this service to consumers at all times of the day. NLP can help businesses in customer experience analysis based on certain predefined topics or categories.

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. 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. 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.

Natural language processing techniques can be presented through the example of Mastercard chatbot. The bot was compatible when it came to comparing it with Facebook messenger but when it was virtual assistant when comparing it with Uber’s bot. When this was about the NLP system gathering data, the text analytics helps in keywords extraction and finding structure or patterns in the unstructured data.

Natural Language Processing is what computers and smartphones use to understand our language, both spoken and written. Because we use language to interact with our devices, NLP became an integral part of our lives. NLP can be challenging to implement correctly, you can read more about that here, but when’s it’s successful it offers awesome benefits. We were blown away by the fact that they were able to put together a demo using our own YouTube channels on just a couple of days notice. 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.

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In turn, this allows them to make improvements to their offering to serve their customers better and generate more revenue. Thus making social media listening one of the most important examples of natural language processing for businesses and retailers. 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. 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. It’s an intuitive behavior used to convey information and meaning with semantic cues such as words, signs, or images.

Speech Recognition

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. 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. Users of productivity applications ranging from word processors to text entry boxes on a smartphone will doubtless be familiar with features such as autocorrect, which amends text as you’re typing or dictating it.

When a chatbot is successfully able to break down these two parts in a query, the process of answering it begins. NLP engines are individually programmed for each intent and entity set that a business would need their chatbot to answer. SaaS tools, on the other hand, are ready-to-use solutions that allow you to incorporate NLP into tools you already use simply and with very little setup. Connecting SaaS tools to your favorite apps through their APIs is easy and only requires a few lines of code. It’s an excellent alternative if you don’t want to invest time and resources learning about machine learning or NLP.

Below, you can find a comparative analysis for the common network-based models and some advice on how to work with them. First of all, you need to have a clear understanding of the purpose that the engine will serve. We suggest you start with a descriptive analysis to find out how often a particular part of speech occurs.

Instead of wasting time navigating large amounts of digital text, teams can quickly locate their desired resources to produce summaries, gather insights and perform other tasks. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.


Called DeepHealthMiner, the tool analyzed millions of posts from the Inspire health forum and yielded promising results. Still, as we’ve seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results. They are beneficial for eCommerce store owners in that they allow customers to receive fast, on-demand responses to their inquiries. This is important, particularly for smaller companies that don’t have the resources to dedicate a full-time customer support agent. Features like autocorrect, autocomplete, and predictive text are so embedded in social media platforms and applications that we often forget they exist. Autocomplete and predictive text predict what you might say based on what you’ve typed, finish your words, and even suggest more relevant ones, similar to search engine results.

  • Stemming “trims” words, so word stems may not always be semantically correct.
  • Your chatbot must be able to understand what the users say or want to do in order to answer queries, search from a domain knowledge base, and conduct numerous other actions in order to continue dialogues with the user.
  • 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.
  • Entities can be names, places, organizations, email addresses, and more.
  • Translation company Welocalize customizes Googles AutoML Translate to make sure client content isn’t lost in translation.

Read more about https://www.metadialog.com/ here.

nlp engines examples


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