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

TL;DR

This paper introduces NETHIC, an automatic text classification tool that combines neural networks, hierarchical taxonomies, and document embeddings to achieve effective and efficient classification, validated through experiments on diverse corpora.

Contribution

The paper presents a novel text classification method integrating neural networks with hierarchical taxonomies and document embeddings, enhancing performance and scalability.

Findings

NETHIC outperforms baseline models in accuracy.

Adding document embeddings improves classification performance.

The tool is effective on both generic and domain-specific corpora.

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.