An Approach to Email Categorization and Response Generation

Sasa Arsovski1, Muniru Idris Oladele2, Adrian David Cheok2, Velibor Premcevski3 and Branko Markoski3

  1. RAffles University, Menara Kotaraya
    Menara Kotaraya, Level 9, #09, 01, Jalan Trus, Bandar Johor Bahru, 80000 Johor Bahru, Johor
    sasa.arsovski@gmail.com
  2. Imagineering Institute, Johor Malaysia
    Anchor 5, Mall of Medini, 4, Lebuh Medini Utara, 79200 Nusajaya, Johor
    Idris@imagineeringinstitute.org, adrian@imagineeringinstitute.org
  3. University of Novi Sad, Technical Faculty "Mihajlo Pupin"
    23000 Zrenjanin, Serbia
    velibor.premcevski@tfzr.rs, markoni@uns.ac.rs

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.

Key words

Email responder, Deep learning, AIML, Chatbot

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS211101009A

Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Arsovski, S., Oladele, M. I., Cheok, A. D., Premcevski, V., Markoski, B.: An Approach to Email Categorization and Response Generation. Computer Science and Information Systems, Vol. 19, No. 2, 913–934. (2022), https://doi.org/10.2298/CSIS211101009A