RT Journal Article
SR Electronic
A1 Koukal, Filip
A1 Dařena, František
A1 Ježdík, Roman
A1 Přichystal, Jan
T1 Improving Automated Categorization of Customer Requests with Recent Advances in Natural Language Processing
JF European Journal of Business Science and Technology
YR 2024
VO 10
IS 2
SP 173
OP 184
DO 10.11118/ejobsat.2024.010
UL https://ejobsat.cz/artkey/ejo-202402-0001.php
AB In this paper, we focus on the categorization of tickets in service desk systems. We employ modern neural network-based artificial intelligence methods to improve the performance of current systems and address typical problems in the domain. Special attention is paid to balancing the ticket categories, selecting a suitable representation of text data, and choosing a classification model. Based on experiments with two real-world datasets, we conclude that text preprocessing, balancing the ticket categories, and using the representations of texts based on fine-tuned transformers are crucial for building successful classifiers in this domain. Although we could not directly compare our work to other research the results demonstrate superior performance to similar works.