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arXiv · 2603.11770

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

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

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Luigi Lomasto, Rosario Di Florio, Andrea Ciapetti, Giuseppe Miscione, Giulia Ruggiero, Daniele Toti. 2026-03-12. An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool. https://doi.org/10.1007/978-3-030-40783-4_4

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