An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool

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

Risultato della ricerca: Contributo in libroContributo a convegno

1 Citazioni (Scopus)

Abstract

This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the inner capabilities of highly-scalable neural networks combined with the expressiveness of hierarchical taxonomies. As such, NETHIC succeeds in bringing about a mechanism for text classification that proves to be significantly effective as well as efficient. The tool had undergone an experimentation process against both a generic and a domain-specific corpus, outputting promising results. On the basis of this experimentation, NETHIC has been now further refined and extended by adding a document embedding mechanism, which has shown improvements in terms of performance on the individual networks and on the whole hierarchical model.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Business Information Processing
Pagine57-77
Numero di pagine21
Volume378
DOI
Stato di pubblicazionePubblicato - 2020
Evento21st International Conference on Enterprise Information Systems, ICEIS 2019 - grc
Durata: 3 mag 20195 mag 2019

Serie di pubblicazioni

NomeLECTURE NOTES IN BUSINESS INFORMATION PROCESSING

Convegno

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

Keywords

  • Document embedding
  • Machine learning
  • Neural networks
  • Taxonomies
  • Text classification

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