Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Martins, S., Garrido, N. & Sebastião, P. (2023). Port request classification automation through NLP. In Procedia Computer Science. (pp. 1-8).: Elsevier.
Exportar Referência (IEEE) 
S. A. Martins et al.,  "Port request classification automation through NLP", in Procedia Computer Science, Elsevier, 2023, vol. 2023, pp. 1-8
Exportar BibTeX 
@inproceedings{martins2023_1731982808380,
	author = "Martins, S. and Garrido, N. and Sebastião, P.",
	title = "Port request classification automation through NLP",
	booktitle = "Procedia Computer Science",
	year = "2023",
	editor = "",
	volume = "2023",
	number = "",
	series = "",
	pages = "1-8",
	publisher = "Elsevier",
	address = "",
	organization = "",
	url = "https://www.sciencedirect.com/journal/procedia-computer-science"
}
Exportar RIS 
TY  - CPAPER
TI  - Port request classification automation through NLP
T2  - Procedia Computer Science
VL  - 2023
AU  - Martins, S.
AU  - Garrido, N.
AU  - Sebastião, P.
PY  - 2023
SP  - 1-8
SN  - 1877-0509
UR  - https://www.sciencedirect.com/journal/procedia-computer-science
AB  - This project 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  -