Improving Automated Categorization of Customer Requests with Recent Advances in Natural Language Processing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F24%3A43926267" target="_blank" >RIV/62156489:43110/24:43926267 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.11118/ejobsat.2024.010" target="_blank" >https://doi.org/10.11118/ejobsat.2024.010</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.11118/ejobsat.2024.010" target="_blank" >10.11118/ejobsat.2024.010</a>
Alternative languages
Result language
angličtina
Original language name
Improving Automated Categorization of Customer Requests with Recent Advances in Natural Language Processing
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Journal of Business Science and Technology
ISSN
2336-6494
e-ISSN
2694-7161
Volume of the periodical
10
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
12
Pages from-to
173-184
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85214573059