NETHIC: A system for automatic text classification using neural networks and hierarchical taxonomies

Daniele Toti, Andrea Ciapetti, Rosario Di Florio, Luigi Lomasto, Giuseppe Miscione, Giulia Ruggiero

Risultato della ricerca: Contributo in libroContributo a convegno

5 Citazioni (Scopus)

Abstract

This paper presents NETHIC, a software system for the automatic classification of textual documents based on hierarchical taxonomies and artificial neural networks. This approach combines the advantages of highly-structured hierarchies of textual labels with the versatility and scalability of neural networks, thus bringing about a textual classifier that displays high levels of performance in terms of both effectiveness and efficiency. The system has first been tested as a general-purpose classifier on a generic document corpus, and then applied to the specific domain tackled by DANTE, a European project that is meant to address criminal and terrorist-related online contents, showing consistent results across both application domains.
Lingua originaleEnglish
Titolo della pubblicazione ospiteICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems
Pagine284-294
Numero di pagine11
Volume1
Stato di pubblicazionePubblicato - 2019
Evento21st International Conference on Enterprise Information Systems, ICEIS 2019 - grc
Durata: 3 mag 20195 mag 2019

Serie di pubblicazioni

NomeICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems

Convegno

Convegno21st International Conference on Enterprise Information Systems, ICEIS 2019
Cittàgrc
Periodo3/5/195/5/19

Keywords

  • Machine Learning
  • Neural Networks
  • Taxonomies
  • Text Classification

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