Natural language understanding (NLU) is the technology that enables a chatbot to understand the messages it receives from humans. It is a subset of natural language processing (NLP) that utilises advanced artificial intelligence algorithms to understand and interpret the subtleties in language.
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In 1964, MIT students attempted to create the very first natural language understanding algorithm. The technology evolved rapidly over the next decade, starting with Joseph Weizenbaum's notorious ELIZA chatbot in 1965 all the way to Michael Dyer's BORIS system in 1983.
Today, variations of NLU are present in most NLP engines -- including ubisend's!
While NLU focuses on understanding what the input means, NLP has two very different jobs. The first is to dissect what is said (e.g. is it a question? Small talk? What is it about? Weather, Manhattan, job opportunities?). The second is to process this information and return an answer that makes sense in the context it gathered.
In enterprise-grade chatbots, natural language process and natural language understanding work hand in hand to enable the program to understand human input.
Learn more about natural language processing.
NLU enables a chatbot to understand the context behind a message, instead of taking each message in a vacuum.
NLU enablers a chatbot to understand sophisticated and intricate input from humans, instead of 'simple' phrase matching.
NLU enables a chatbot to deal with unpredictable inputs such as spelling mistakes, cursing, poor grammar, etc.
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