Computer Science and Information Systems volume: 19 number: 2 startpage: 913 endpage: 934 info:eu-repo/semantics/article Keywords: email responder, deep learning, AIML, chatbot http://creativecommons.org/licenses/by/4.0/legalcode application/pdf 971398 bytes eng 2022 An Approach to Email Categorization and Response Generation Abstract. The creation of automatic e-mail responder systems with human-quality responses is challenging due to the ambiguity of meanings and difficulty in response modeling. In this paper, we present the Personal Email Responder (PER); a novel system for email categorization and semi-automatic response generation. The key novelty presented in this paper is an approach to email categorization that distinguishes query and non-query email messages using Natural Language Processing (NLP) and Neural Network (NN) methods. The second novelty is the use of Artificial Intelligence Markup Language (AIML)- based chatbot for semiautomatic response creation. The proposed methodology was implemented as a prototype mobile application, which was then used to conduct an experiment. Email messages logs collected in the experimental phase are used to evaluate the proposed methodology and estimate the accuracy of the presented system for email categorization and semi-automatic response generation. https://unilib.phaidrabg.rs/o:6192 doi:10.2298/CSIS211101009A Arsovski, Sasa Oladele, Muniru Idris Cheok, Adrian David Premcevski, Velibor Markoski, Branko