6 Real-World Examples of Natural Language Processing

In this post, I’ll go over four functions of artificial intelligence (AI) and natural language processing and give examples of tools and services that use them. These are the most popular applications of Natural Language Processing and chances are you may have never heard of them! NLP is used in many other areas such as social media monitoring, translation tools, smart home devices, survey analytics, etc.

Using the NLP system can help in aggregating the information and making sense of each feedback and then turning them into valuable insights. This will not just help users but also improve the services rendered by the company. And there are many natural language processing examples that we all are using for the last many years.

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We also have Gmail’s Smart Compose which finishes your sentences for you as you type. Social media monitoring tools can use NLP techniques to extract mentions of a brand, product, or service from social media posts. Once detected, these mentions can be analyzed for sentiment, engagement, and other metrics. This information can then inform marketing strategies or evaluate their effectiveness.

Deep semantic understanding remains a challenge in NLP, as it requires not just the recognition of words and their relationships, but also the comprehension of underlying concepts, implicit information, and real-world knowledge. LLMs have demonstrated remarkable progress in this area, but there is still room for improvement in tasks that require complex reasoning, common sense, or domain-specific expertise. NLP can generate human-like text for applications—like writing articles, creating social media posts, or generating product descriptions. A number of content creation co-pilots have appeared since the release of GPT, such as Jasper.ai, that automate much of the copywriting process.

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NLP can help businesses in customer experience analysis based on certain predefined topics or categories. It’s able to do this through its ability to classify text and add tags or categories to the text based on its content. In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products. Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.

Language models are AI models which rely on NLP and deep learning to generate human-like text and speech as an output. Language models are used for machine translation, part-of-speech (PoS) tagging, optical character recognition (OCR), handwriting 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.

Gmail uses NLP to anticipate what you’ll write in an email and then make suggestions to autofill. This is a very innovative project where you want to produce titles for scientific papers. For this project, a GPT-2 is trained on more than 2,000 article titles extracted from arXiv. You can use this application on other things, like text generating tasks for producing song lyrics, dialogues, etc. From this project, you can also learn about web scraping, because you will need to extract text from research papers in order to feed it to your model for training.

It is equivalent to a boost from around 3 billion USD in 2017 to more than 43 billion in 2025. Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. The overall thread of questions will make it easy to pick one that can solve the purpose of the question letting one come to the conclusion. The MasterCard virtual assistant chatbot can provide a 360 eagle view of the user spending habits along with offering them what benefits they can take from the card.

It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. In conclusion, Natural Language Processing (NLP) revolutionized how we interact with computers, harnessing language’s power for numerous applications. From sentiment analysis and text summarization to machine translation and chatbots, NLP continues to redefine human-computer interaction. Opinion mining enables organizations to acquire valuable understandings of customer preferences, enhance customer experience, and respond effectively to feedback.

The NLP integrated features like autocomplete, autocorrection, spell checkers located in search bars can provide users a way to find & get information in a click. Natural language processing is described as the interaction between human languages and computer technology. Often overlooked or may be used too frequently, NLP has been missed or skipped on many occasions. Throughout the years, they have transformed into a very reliable and powerful friend. From setting our morning alarm to finding a restaurant for us, a voice assistant can do anything.

NLP and Writing Systems

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• These functionalities have the ability to learn and change based on your behavior.
• If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page.
• Of course, smaller survey companies may choose to analyze their data manually to conclude what they need to.