5 real-world applications of natural language processing NLP

Applications of Natural Language Processing and NLP data sets

example of nlp in ai

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. 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. How often have you traveled to a city where you were excited to know what languages they speak? A resume parsing system is an application that takes resumes of the candidates of a company as input and attempts to categorize them after going through the text in it thoroughly. This application, if implemented correctly, can save HR and their companies a lot of their precious time which they can use for something more productive. Looking ahead to the future of AI, two emergent areas of research are poised to keep pushing the field further by making LLM models more autonomous and extending their capabilities.

Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines.

Top 15 Pre-Trained NLP Language Models

Every day humans share a large quality of information with each other in various languages as speech or text. Discover how AI and natural language processing can be used in tandem to create innovative technological solutions. Many modern NLP applications are built on dialogue between a human and a machine. Accordingly, your NLP AI needs to be able to keep the conversation moving, providing additional questions to collect more information and always pointing toward a solution.

  • GPT-3 is a transformer-based NLP model that performs translation, question-answering, poetry composing, cloze tasks, along with tasks that require on-the-fly reasoning such as unscrambling words.
  • NLP allows automatic summarization of lengthy documents and extraction of relevant information—such as key facts or figures.
  • Automatic grammar checking, the task of detecting and correcting grammatical errors and spelling mistakes in text depending on context, is another major part of NLP.

Delivering the best customer experience and staying compliant with financial industry regulations can be driven through conversation analytics. Conversation analytics provides business insights that lead to better patient outcomes for the professionals in the healthcare industry. Improve quality and safety, identify competitive threats, and evaluate innovation opportunities. Repustate has helped organizations worldwide turn their data into actionable insights. Learn how these insights helped them increase productivity, customer loyalty, and sales revenue.

What are NLP tasks?

Customer service and experience are the most important thing for any company. It can help the companies improve their products, and also keep the customers satisfied. But interacting with every customer manually, and resolving the problems can be a tedious task. Chatbots help the companies in achieving the goal of smooth customer experience.

example of nlp in ai

Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience. Additional ways that NLP helps with text analytics are keyword extraction and finding structure or patterns in unstructured text data. There are vast applications of NLP in the digital world and this list will grow as businesses and industries embrace and see its value. While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation.

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example of nlp in ai

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