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Publication Detailed Description
Procedia Computer Science
Year (definitive publication)
2024
Language
English
Country
Netherlands
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Abstract
This paper describes a suggested prototype to carry out the automatic classification of requests from a Port Help Desk. It intents to ascertain if the implementation of this framework is viable for this sector. For this purpose different models were employed, such as SVM, Decision Tree, Random Forest, LSTM, BERT and a SVM hierarchical model. To verify their efficiency these models were evaluated using Precision, Recall and F1-Score metrics. We obtained F1-Scores of 94.36% and 92.48% when classifying the request’s category and group respectively. A F1-Score of 93.41% while using a SVM model for category classification when employing a hierarchical classification architecture.
Acknowledgements
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Keywords
NLP,Request classification,Help desk,Machine learning,Port systems
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
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