An Overview of Chatbot Structure and Source Algorithms

Abstract

Chatbots or Chat Robots are the technology of the decade in which a machine having human conversation capabilities interacts with people. The technology uses various Human-Computer Interaction (HCI) mechanisms to communicate with humans. Chatbot technology has grown a lot in the past few years and is used in various fields such as medical, e-commerce, education, and information retrieval. This study aims to understand the existing chatbot conceptual design and explains the overall architecture of the chatbot, which comprises four components of basic input processing, input comprehension, information retrieval, and answer selection and production. Also, the data flow for the chatbot has been evaluated in this study. Furthermore, the portion of a study concentrated on investigating various methods employed by earlier chatbot structures. Natural Language Processing, Pattern Matching Algorithm, Naive Bayes Algorithm, Sequence to Sequence Model, Hybrid Emotion Interference Model, and Long Short-Term Memory are among them. Also, the elements influencing chatbot design have been discovered, which include the manner of input, the usage of Artificial intelligence Mark-up Language (AIML), SQL, the role of user technology acceptance, and the selection of techniques and application sectors. To summarize, this work provides a summary of research on chatbot structure and identifies the most prevalent algorithms employed with the current chatbot structure.