NETHIC: A System for Automatic Text Classification using Neural Networks and Hierarchical Taxonomies

Abstract

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 highlystructured 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 terroristrelated online contents, showing consistent results across both application domains. This paper is structured as follows. In Section 2, 296

Model of the text classification system using fuzzy sets

“…In [23] a keyword recognition system for texts using neural networks and hierarchical taxonomies was described. For each category in taxonomy own pre-trained neural network is used.…”