Natural language processing ensures that AI can understand the natural human languages people speak every day. Learn more about this impactful AI subfield.
Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether you write it, speak it, or even scribble it. As AI-powered devices and services become increasingly more intertwined with your daily life and the world, so too does the impact that NLP has on ensuring a seamless human-computer experience.
Learn more about what NLP is, the techniques used to create it, and some of the benefits it provides consumers and businesses. Discover common NLP tools and explore some online, cost-effective courses that can introduce you to one of the field’s most fundamental concepts.
Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, comprehensible to computers.
NLP is used in a wide variety of everyday products and services. Some of the most common technologies that use NLP are voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages.
NLP encompasses a wide range of techniques to analyze human language. Some of the most common techniques you will likely encounter in the field include:
Sentiment analysis: An NLP technique that analyzes text to identify its sentiments, such as “positive,” “negative,” or “neutral.” Sentiment analysis is commonly used by businesses to better understand customer feedback.
Summarization: An NLP technique that summarizes a longer text in order to make it more manageable for time-sensitive readers. Some common texts this technology can summarize include reports and articles.
Keyword extraction: An NLP technique that analyzes a text to identify the most important keywords or phrases. Keyword extraction is commonly used for search engine optimization (SEO), social media monitoring, and business intelligence purposes.
Tokenization: The process of breaking characters, words, or subwords down into “tokens” that the program can analyze. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence.
Whether it’s being used to quickly translate a text from one language to another or producing business insights by running sentiment analysis on hundreds of reviews, NLP provides both businesses and consumers with a variety of benefits.
Unsurprisingly, then, you can expect to see more of it in the coming years. According to research by Fortune Business Insights, they project the global market for NLP to grow from $29.71 billion in 2024 to $158.04 billion in 2032 [1].
Some common benefits of NLP include:
The ability to analyze both structured and unstructured data, such as speech, text messages, and social media posts.
Improving customer satisfaction and experience by identifying insights using sentiment analysis.
Reducing costs by employing NLP-enabled AI to perform specific tasks, such as chatting with customers via chatbots or analyzing large amounts of text data.
Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys, and reviews.
NLP can be used for a wide variety of applications, but it's far from perfect. In fact, many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors, and other types of ambiguous statements. This means that NLP is mostly limited to unambiguous situations that don't require a significant amount of interpretation.
Although natural language processing might sound like something out of a science fiction novel, the truth is that NLP examples already exist in your everyday life as you interact with countless NLP-powered devices and services every day.
Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products. While chatbots can’t answer every question that customers may have, businesses like them because they offer cost-effective ways to troubleshoot common problems or questions that consumers have about their products.
Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed up their writing process and correct common typos.
ChatGPT—a chatbot powered by AI and natural language processing—produces unusually human-like responses. Recently, it has dominated headlines due to its ability to produce responses that far outperform what was previously commercially possible.
If you'd like to learn more, the University of Michigan's ChatGPT Teach Out brings together experts on communication technology, the economy, artificial intelligence, natural language processing, health care delivery, and law to discuss the impacts of the technology now and into the future.
You can choose from the numerous natural language processing tools and services available to help you start working with NLP today. Some of the most common tools and services you might encounter include the following:
Google Cloud NLP API
IBM Watson
Amazon Comprehend
Python is a programming language well-suited to NLP. Some common Python libraries and toolkits you can use to start exploring NLP include NLTK, Stanford CoreNLP, and Genism.
Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions. Start exploring the field in greater depth by taking a cost-effective, flexible Specialization on Coursera.
DeepLearning.AI’s Natural Language Processing Specialization can help you prepare to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and build chatbots. In DeepLearning.AI’s Machine Learning Specialization, meanwhile, you can have the opportunity to master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, three-course program by AI visionary (and Coursera co-founder) Andrew Ng.
Fortune Business Insights. “The global natural language processing (NLP) market, https://www.fortunebusinessinsights.com/industry-reports/natural-language-processing-nlp-market-101933.” Accessed February 4, 2025.
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