This paper presents NETHIC, a software system for the automatic classification of textual documents based on hierarchical taxonomies and artificial neural networks. This approach combines the advantages of highly-structured hierarchies of textual labels with the versatility and scalability of neural networks, thus bringing about a textual classifier that displays high levels of performance in terms of both effectiveness and efficiency. The system has first been tested as a general-purpose classifier on a generic document corpus, and then applied to the specific domain tackled by DANTE, a European project that is meant to address criminal and terrorist-related online contents, showing consistent results across both application domains.
|Nome||ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems|
|Convegno||21st International Conference on Enterprise Information Systems, ICEIS 2019|
|Periodo||3/5/19 → 5/5/19|
- Machine Learning
- Neural Networks
- Text Classification