Knowledge-base Optimization to Reduce the Response Time of Bangla Chatbot

Abstract

Chatbots have been very popular in recent years for being able to serve as a customer representative, a language learner and so forth. Long short-term memory abbreviated as LSTM is a ubiquitous artificial recurrent neural network that is frequently being used for the chatbot. Nevertheless, if a user makes the line break of sequence, then it is rare to inform the right information without the impact of the previous sequence. As a result, in case of a help desk chatbot, LSTM is not the best option for taking steps of the right information. On the other hand, mathematical and statistical procedures are prominently useful for providing the proper knowledge without having back the impact of sequence. Still, it takes more execution time to respond. The goal of this paper is to present the optimal chatbot for the lowest execution time and three mathematical and statistical strategies for Bangla Intelligence chatbot in light of information obtained from Noakhali Science and Technology University (NSTU). As the procedures, we have followed cosine similarity, Jaccard similarity, and Naive Bayes classifier. To reduce the response time, we decorated the whole path into a 3-depth tree, such as a question, topic, and answer. We have compared the performance of the selected strategies where the best accuracy was 93.22% using the cosine similarity. Contribution-This paper presents techniques to reduce the response time of statistical and mathematical Bangla Chatbot.