An Investigation on Question Answering for an Online Feedable Chatbot

This thesis presents the design and implementation of a Chatbot that is able to answer questions about an entity it is learning about. This Chatbot is capable of automatically generating multiple genres using a unique technique to populate its SQL database from the Web. Our Online Feedable Chatbot can hold a conversation with the user regarding the information it has extracted from the Web. Our Online Feedable Chatbot attempts to create Question Answer pairs (QAPs) and acquire imperative sentences specially targeted at the entity it gives information about. A method to select the best response for a Chatbot query among a set of sentences using hybrid terms, syntactic, and semantic extracted features is developed as a response search system of our Online Feedable Chatbot. This tutor Chatbot can expand its training knowledge base by automatically extracting more QAPs and imperative sentences from the Web whenever the user needs to learn about a new entity and without any instructor's supervision, amendments, or control.