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While there are many challenges in natural language processing, the benefits of NLP for businesses are huge making NLP a worthwhile investment. Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods. Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. NLP has existed for more than 50 years and has roots in the field of linguistics.<\/p>\n<\/p>\n
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Typical purposes for developing and implementing a controlled natural language are to aid understanding by non-native speakers or to ease computer processing. An example of a widely-used controlled natural language is Simplified Technical English, which was originally developed for aerospace and avionics industry manuals. You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools.<\/p>\n<\/p>\n
Learn how to extract and classify text from unstructured data with MonkeyLearn\u2019s no-code, low-code text analysis tools. With natural language processing and machine learning working behind the scenes, all you need to focus on is using the tools and helping them to improve their natural language understanding. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. When it comes to examples of natural language processing, search engines are probably the most common.<\/p>\n<\/p>\n