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

arXiv cs.AI / 3/13/2026

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Key Points

Introduces NETHIC, an automatic text classification method implemented as a software tool that leverages scalable neural networks.
Combines neural networks with hierarchical taxonomies to enhance classification expressiveness and efficiency.
Adds a document embedding mechanism that improves performance for both individual networks and the overall hierarchical model, based on experiments with generic and domain-specific corpora.
The work is announced as a new arXiv preprint (v1) and shows promising results, indicating potential for broader adoption and further refinements.

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.