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Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy
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作者 Mukhtar Ghaleb Yahya Almurtadha +5 位作者 Fahad Algarni Monir Abdullah Emad Felemban Ali M.Alsharafi Mohamed Othman khaled ghilan 《Computers, Materials & Continua》 SCIE EI 2022年第2期2619-2638,共20页
People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experien... People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic. 展开更多
关键词 Machine learning text classification e-health chatbot COVID-19 awareness natural language understanding
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