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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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.
Original languageEnglish
Title of host publicationLecture Notes in Business Information Processing
Pages57-77
Number of pages21
Volume378
DOIs
Publication statusPublished - 2020
Event21st International Conference on Enterprise Information Systems, ICEIS 2019 - grc
Duration: 3 May 20195 May 2019

Publication series

NameLECTURE NOTES IN BUSINESS INFORMATION PROCESSING

Conference

Conference21st International Conference on Enterprise Information Systems, ICEIS 2019
Citygrc
Period3/5/195/5/19

Keywords

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

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