A Subject-Specific Chatbots for Primary Education End-users using Machine Learning Techniques

  • Dr. B. Santhosh Kumar, N.Kanagavalli, T.Daniya

Abstract

Researchers have studied chatbots and other types of conversational agents for decades, starting with the well-known chatbot ELIZA. Chatbots are the taxons of conversational agents that are designed to communicate with clients using natural language. The best source of solutions to the user’s queries in any domain is ultimately chatbot. Mostly Chatbots used for acquiring knowledge. It can be implemented on our mobiles and local personal systems and can access the internet. Chatbots communicates with clients in any particular domain with their query as input in general conversational statements. When the user starts asking questions on some specific topic the discussion begins, and the chatbot starts answering their queries. Chatbots are the software entities which act as human entity. An efficient response will generate the client’s question based on the predefined knowledge database. Initially, chatbots developed for entertainment purposes, and they used some keyword matching algorithms to find a response to the user’s query, e.g., ELIZA. Nowadays, with the improvement of natural language processing based machine-learning techniques, chatbots able to make better decisions. Even though some pattern matching rules can apply to the user's query and an appropriate response can be generated, but the performance of the chatbot will be less because they won’t store the chat history and also, to be frank, they will not understand what you asked. Based on knowledgebase, the responses generated. In the context of Chatbot, a metric is used to measure the performance of a chatbot.

Published
2020-04-13
How to Cite
Dr. B. Santhosh Kumar, N.Kanagavalli, T.Daniya. (2020). A Subject-Specific Chatbots for Primary Education End-users using Machine Learning Techniques. International Journal of Control and Automation, 13(02), 407 - 415. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/9119
Section
Articles