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Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities.\r\nBeyond chatbots, Conversational AI refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale.\r\nThe process of building, testing and deploying chatbots can be done on cloud-based chatbot development platforms offered by cloud Platform as a Service (PaaS) providers such as Oracle Cloud Platform SnatchBot and IBM Watson. 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Formulating responses to questions in natural language is one of the most typical examples of Natural Language Processing applied in various enterprises’ end-use applications.\r\n<span style=\"font-weight: bold;\">How does a chatbot work?</span>\r\nThere are two different tasks at the core of a chatbot:\r\n<ol><li>user request analysis</li><li>returning the response</li></ol>\r\nUser request analysis: this is the first task that a chatbot performs. It analyzes the user’s request to identify the user intent and to extract relevant entities.\r\nThe ability to identify the user’s intent and extract data and relevant entities contained in the user’s request is the first condition and the most relevant step at the core of a chatbot: If you are not able to correctly understand the user’s request, you won’t be able to provide the correct answer.\r\nReturning the response: once the user’s intent has been identified, the chatbot must provide the most appropriate response for the user’s request. 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