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Problem classification for tailored help desk auto replies

Nicholls, Reece; Fellows, Ryan; Battle, Steve; Ihshaish, Hisham


Reece Nicholls

Ryan Fellows


Plamen Angelov

Chrisina Jayne

Antonios Papaleonidas


IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-reply may include generic ‘boiler-plate’ text that addresses common problems of the day, with relevant information and links. The approach explored here is to tailor the content of the auto-reply to the user’s problem, so as to increase the relevance of the information included. Problem classification is achieved by training a neural network on a suitable corpus of IT helpdesk email data. While this is no substitute for follow-up by helpdesk agents, the aim is that this system will provide a practical stop-gap.


Nicholls, R., Fellows, R., Battle, S., & Ihshaish, H. (2022). Problem classification for tailored help desk auto replies. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 (445-454).

Conference Name Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK
Conference Location Bristol, UK,
Start Date Sep 6, 2022
End Date Sep 9, 2022
Acceptance Date Jul 12, 2022
Online Publication Date Sep 7, 2022
Publication Date Sep 7, 2022
Deposit Date Sep 23, 2022
Publicly Available Date Sep 8, 2024
Publisher Springer Verlag
Volume 13532 LNCS
Pages 445-454
Series Title Lecture Notes in Computer Science (LNCS, volume 13532)
Series Number 13532
Series ISSN 1611-3349; 0302-9743
Edition Vol 13532
Book Title Artificial Neural Networks and Machine Learning – ICANN 2022
Chapter Number 37
ISBN 9783031159367
Keywords neural networks, data augmentation, helpdesk, supervised learning
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