Natural language processing is a class of technology that seeks to process, interpret and produce natural languages such as English, Mandarin Chinese, Hindi and Spanish. Real world use of natural language doesn't follow a well formed set of rules and exhibits a large number of variations, exceptions and idiosyncratic qualities. As such, natural language processing is often tackled with artificial intelligence techniques designed to automate the learning process. The following are common types of natural language processing.
Optical Character RecognitionConverting written or printed text into data.
Speech RecognitionConverting spoken words into data.
Machine TranslationTranslating text from one language to another.
Natural Language GenerationFormatting information as a natural language. For example, a weather system that generates a weather forecast in paragraph form.
Interpreting basic information from language such as the topic being discussed and whether comments are positive or negative. For example, brand engagement metrics might look at how many people are talking about a brand and how much of that talk is positive.Understanding and answering questions posed in a natural language. For example, accurately processing a search query such as "give me the address of that bar I went to last weekend."
Machine LearningUsing natural language to train artificial intelligence. This can be learning related to language itself or another topic such as economics.
Natural Language ProgrammingTools that allow end users to create or customize computer programs with natural language. For example "make me an app that suggests new careers paths to me based on people with similar skills and experiences who have successfully improved their salary and quality of life with a new career."
Affective computing is the machine equivalent of emotional intelligence. Allows user interfaces to understand human ideas and emotional states and to take on human-like conversations.
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