An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911508535967744 |
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| author | Lomasto, Luigi Di Florio, Rosario Ciapetti, Andrea Miscione, Giuseppe Ruggiero, Giulia Toti, Daniele |
| author_facet | Lomasto, Luigi Di Florio, Rosario Ciapetti, Andrea Miscione, Giuseppe Ruggiero, Giulia Toti, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11770 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool Lomasto, Luigi Di Florio, Rosario Ciapetti, Andrea Miscione, Giuseppe Ruggiero, Giulia Toti, Daniele Artificial Intelligence Computation and Language 68T01, 68T05 I.2.0; I.2.6; I.2.7 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. |
| title | An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool |
| topic | Artificial Intelligence Computation and Language 68T01, 68T05 I.2.0; I.2.6; I.2.7 |
| url | https://arxiv.org/abs/2603.11770 |