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To do this, conversational interfaces use [[Natural_language_processing|Natural Language Processing]] (NLP) to allow computers to understand, analyze and create meaning from human language. Unlike word processors, NLP considers the structure to human language (i.e. Words make a phrase, phrases make sentences which convey the idea or intent the user is trying to invoke). The ambiguous nature of human language makes it difficult for a machine to always correctly interpret the user’s requests, which is why we have seen a shift towards [[Natural_language_understanding|Natural Language Understanding]] (NLU)<ref>{{cite web|url=• https://chatbotsmagazine.com/conversational-interfaces-the-future-of-chatbots-18975a91fe5a}}</ref>.
NLU allows for [[sentiment analysis]] and conversational searches which allows a line of questioning to continue, with the context carried throughout the conversation. NLU allows conversational interfaces to handle unstructured inputs that the human brain is able to understand such as spelling mistakes of follow-up questions <ref>{{cite web|url=• https://medium.com/@lolatravel/nlp-vs-nlu-whats-the-difference-d91c06780992</ref>. For example, through leveraging NLU, a user could first ask for the population of the United States. If the user then asks “Who is the president”, the search will carry forward the context of the United States and provide the appropriate response.
Conversational interfaces over the past few years have emerged as a tool for businesses to efficiently provide consumers with relevant information, in a cost effective manner.
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