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This paper introduces NETHIC, an automatic text classification tool leveraging neural networks and hierarchical taxonomies for improved effectiveness and efficiency. The tool incorporates a document embedding mechanism to enhance performance on both individual networks and the overall hierarchical model. Experiments on generic and domain-specific corpora demonstrate promising results, validating the approach.
Hierarchical taxonomies and document embeddings significantly boost the performance of neural networks in text classification, as demonstrated by the NETHIC tool.
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.
