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How Is NLP Used In Real Life?

Natural language processing, or NLP, is a technology powered by artificial intelligence that enables computers to understand the meaning, intention, and sentiment of spoken and written language. NLP combines machine learning, deep learning, and computational linguistics to process human language for a wide range of beneficial technologies. In everyday life, NLP powers solutions like Amazon’s Alexa and Apple’s Siri as well as language translation programs, voice-activated GPS apps, Chatbots on websites, and dictation features on computers and smartphones.

What is NLP, and how does it work?

In NLP solutions, computational linguistic methods create frameworks that allow machines to understand language in human conversation. Machine learning technologies help computers continually improve the understanding of language and comprehension of language elements like metaphors, humor, and grammar variations. Deep learning technology replicates the neural network of the human brain and teaches computers to learn and think like humans, enabling machines to recognize, classify, and correlate language patterns. Using these technologies, NLP solutions transform spoken and written text into machine-readable data and apply algorithms that reveal the meaning of words and sentences along with the intention and emotion behind them.

How is NLP used in real-life business situations?

Because it can quickly digest, analyze, and understand massive amounts of text, NLP technology can be indispensable in addressing many business challenges.

  • Text extraction. NLP applications can automatically review large and summarize large numbers of documents far faster than humans.
  • Translation. NLP technology can accurately translate text from one language to another, finding the best way to communicate thought and emotion in the new language rather than simply translating text word for word.
  • Customer service. Voice assistants and chatbots use NLP technology when interacting with customers to help find resources, answer questions, and resolve issues faster.
  • Proofreading. Tools for proofreading text rely on NLP to understand the meaning of written text.
  • Sentiment analysis. NLP technology enables businesses to analyze phone conversations, social media posts, online reviews, and other communications to determine how customers feel about a brand. Sentiment analysis can reveal how customer feel about specific products, services, and communications, providing insights that ultimately help to better fulfill customer expectations.
  • Email filtering. NLP helps filter unwanted messages from email inboxes by searching for language that is characteristic of spam.
  • Predictive text. NLP is behind the predictive text features on smart phones that automatically suggest the next word based on the text a user has already entered.

How is NLP used in contact centers?

Contact centers use NLP-powered technologies to automate the analysis of text-based and audio conversations with customers. Because NLP solutions can process and understand conversations at scale, contact centers can extract insight and intelligence from every customer interaction rather than sampling a small fraction through manual review. This comprehensive approach to mining customer conversations provides much deeper insight into what customers want, need, and expect, helping businesses to improve the customer journey, enhance customer satisfaction, and build greater customer loyalty.

What is conversation analytics vs. NLP?

Conversation analytics is technology that combines text analytics and speech analytics, enabling companies to capture, transcribe, analyze, and extract insight from customer conversations at scale. Conversation analytics relies on NLP as well as AI and machine learning to accurately determine the meaning, intent, and emotion in customer conversations.

How is NLP used in the CallMiner Eureka platform?

Powered by NLP, artificial intelligence, and machine learning, CallMiner Eureka is the most comprehensive platform for analyzing omnichannel interactions at scale. CallMiner makes it possible to analyze 100% of customer conversations from calls, chat, email, SMS text, web, social media, and more, automatically extracting insight that can lead to transformational business change. The Eureka platform uses a speech-to-text engine that combines deep neural networks, machine learning, and NLP technology to achieve extraordinary levels of accuracy in transcription and sentiment analysis. An automated categorization engine merges keyword and phrase identification with word tempo, agitation, silence, and topic mapping to generate actionable insights that can drive business improvement.