Natural language processing definition\n\nNatural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training a computer to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all powered by the technology.\n\nWhile the term originally referred to a system\u2019s ability to read, it\u2019s since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) \u2014 a computer\u2019s ability to create communication of its own \u2014 and natural language understanding (NLU) \u2014 the ability to understand slang, mispronunciations, misspellings, and other variants in language.\n\nHow natural language processing works\n\nNLP works through machine learning (ML). Machine learning systems store words and the ways they come together just like any other form of data. Phrases, sentences, and sometimes entire books are fed into ML engines where they\u2019re processed using grammatical rules, people\u2019s real-life linguistic habits, or both. The computer then uses this data to find patterns and extrapolate what comes next. Take translation software, for example: In French, \u201cI\u2019m going to the park\u201d is \u201cJe vais au parc,\u201d so machine learning predicts that \u201cI\u2019m going to the store\u201d will also begin with \u201cJe vais au.\u201d All the computer needs after that is the word for \u201cstore.\u201d\n\nNLP applications\n\nMachine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're feeding data into the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to better search in the future.\n\nChatbots work the same way: They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like \u201cHey, Alexa.\u201d That\u2019s why critics say these programs are always listening: If they weren\u2019t, they\u2019d never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.\n\nNatural language processing examples\n\nData comes in many forms, but the largest untapped pool of data consists of text. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:\n\nNatural language processing software\n\nWhether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:\n\nNatural language processing courses\n\nThere are many resources available for learning to create and maintain NLP applications and a number of them are free. They include:\n\nNLP salaries\n\nHere are some of the most popular job titles related to NLP and the average salary for each position, according to data from PayScale.