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Export Reference (APA) 
Martins, S., Garrido, N. & Sebastião, P. (2024). Port request classification automation through NLP. In Maria Manuela Cruz-Cunha, Dulce Domingos, Emanuel Peres, Rui Rijo (Ed.), Procedia Computer Science. (pp. 1927-1934). Porto: Elsevier.
Export Reference (IEEE) 
S. A. Martins et al.,  "Port request classification automation through NLP", in Procedia Computer Science, Maria Manuela Cruz-Cunha, Dulce Domingos, Emanuel Peres, Rui Rijo, Ed., Porto, Elsevier, 2024, vol. 239, pp. 1927-1934
Export BibTeX 
@inproceedings{martins2024_1764926839647,
	author = "Martins, S. and Garrido, N. and Sebastião, P.",
	title = "Port request classification automation through NLP",
	booktitle = "Procedia Computer Science",
	year = "2024",
	editor = "Maria Manuela Cruz-Cunha, Dulce Domingos, Emanuel Peres, Rui Rijo",
	volume = "239",
	number = "",
	series = "",
	doi = "10.1016/j.procs.2024.06.376",
	pages = "1927-1934",
	publisher = "Elsevier",
	address = "Porto",
	organization = "",
	url = "https://www.sciencedirect.com/journal/procedia-computer-science"
}
Export RIS 
TY  - CPAPER
TI  - Port request classification automation through NLP
T2  - Procedia Computer Science
VL  - 239
AU  - Martins, S.
AU  - Garrido, N.
AU  - Sebastião, P.
PY  - 2024
SP  - 1927-1934
SN  - 1877-0509
DO  - 10.1016/j.procs.2024.06.376
CY  - Porto
UR  - https://www.sciencedirect.com/journal/procedia-computer-science
AB  - 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.
ER  -