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An Approach to Email Categorization and Response Generation
Computer Science and Information Systems, Tome 19 (2022) no. 2 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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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.
Keywords: Email responder, Deep learning, AIML, Chatbot 
@article{CSIS_2022_19_2_a19,
     author = {Sasa Arsovski and Muniru Idris Oladele and Adrian David Cheok and Velibor Premcevski and Branko Markoski},
     title = {An {Approach} to {Email} {Categorization} and {Response} {Generation}},
     journal = {Computer Science and Information Systems},
     year = {2022},
     volume = {19},
     number = {2},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a19/}
}
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Sasa Arsovski; Muniru Idris Oladele; Adrian David Cheok; Velibor Premcevski; Branko Markoski. An Approach to Email Categorization and Response Generation. Computer Science and Information Systems, Tome 19 (2022) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a19/