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.
Abstract (translated)
这项工作描述了一种自动文本分类方法，该方法被实现在名为NETHIC的软件工具中。NETHIC利用了高度可扩展神经网络的内在能力，并结合了分层分类法的表现力，从而成功地创建了一个既有效又高效的文本分类机制。该工具针对通用语料库和特定领域语料库进行了实验测试，取得了令人鼓舞的结果。基于这些实验结果，NETHIC现已进一步改进并扩充，添加了一种文档嵌入机制，在各个单独网络以及整个分层模型上都显示出性能的提升。
URL
https://arxiv.org/abs/2603.11770
