What is Natural Language Processing? Definition and Examples

Written by Coursera • Updated on

Natural language processing ensures that AI can understand the natural human languages we speak everyday. Learn more about this impactful AI subfield.

[Featured Image]: Machine learning engineer working with a form of a  natural processing language.

Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether it be written, spoken, or even scribbled. As AI-powered devices and services become increasingly more intertwined with our daily lives and world, so too does the impact that NLP has on ensuring a seamless human-computer experience.

In this article, you’ll learn more about what NLP is, the techniques used to do it, and some of the benefits it provides consumers and businesses. At the end, you’ll also learn about common NLP tools and explore some online, cost-effective courses that can introduce you to the field’s most fundamental concepts. 

Natural language processing definition

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 ways NLP is used are through voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages.

Natural language techniques 

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 that are summarized 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 can be analyzed by a program. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence. 

NLP benefits 

Whether it’s being used to quickly translate a text from one language to another or producing business insights by running a sentiment analysis on hundreds of reviews, NLP provides both businesses and consumers with a variety of benefits. 

Unsurprisingly, then, we can expect to see more of it in the coming years. According to research by Fortune Business Insights, the North American market for NLP is projected to grow from $26.42 billion in 2022 to $161.81 billion in 2029 [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. 

Natural language processing examples

Although natural language processing might sound like something out of a science fiction novel, the truth is that people already 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 chat bots 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. 

Natural language processing tools

There are numerous natural language processing tools and services available to help you get started today. Some of the most common tools and services you might encounter include the following: 

  • Google Cloud NLP API

  • IBM Watson 

  • Amazon Comprehend 

Natural language processing with Python 

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. 


Read more: What Is Python Used For? A Beginner’s Guide

Learn more with Coursera

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 will prepare you to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. In DeepLearning.AI’s Machine Learning Specialization, meanwhile, you’ll 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.



Natural Language Processing

Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October '21.


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Word2vec, Machine Translation, Sentiment Analysis, Transformers, Attention Models, Word Embeddings, Locality-Sensitive Hashing, Vector Space Models, Parts-of-Speech Tagging, N-gram Language Models, Autocorrect, Word Embedding, Sentiment with Neural Nets, Siamese Networks, Natural Language Generation, Named-Entity Recognition, Reformer Models, Neural Machine Translation, Chatterbot, T5+BERT Models

Article sources

  1. Business Fortune Insights. “The global natural language processing (NLP) market…,  https://www.fortunebusinessinsights.com/industry-reports/natural-language-processing-nlp-market-101933.” Accessed November 18, 2022. 

Written by Coursera • Updated on

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