基于层次分类与嵌入的自动文本分类工具，提升效率与准确率
An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool
- 结合层次分类与神经网络，构建可扩展的文本分类框架
- 引入文档嵌入机制后，模型整体性能显著提升
- 适用于通用与领域特定语料，适合需要高效分类的场景
本文介绍了一款名为NETHIC的自动化文本分类软件工具，该工具利用高度可扩展神经网络的内在能力，结合层次分类体系的表达优势，实现高效且有效的文本分类。NETHIC在通用和领域特定语料上均进行了实验，结果令人鼓舞。在此基础上，工具进一步优化并新增文档嵌入机制，显著提升了各子网络及整体层次模型的性能表现。
原文摘要 · Abstract (English)
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.
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