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Approach for Hierarchical Global All-In Classification with application of Convolutional Neural Networks

机译：应用卷积神经网络的分层全局综合分类方法

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摘要

This paper describes the application of convolutional neural networks adapted for hierarchical text classification task. Even though CNN models already been shown to be efficient for text classification, but not really previously explored in the context of hierarchy. Therefore, more detailed evaluation of experiments with CNN models were required. Our conducted experiments are compared with already existing multiple strategies that use Linear Regression and Support Vector Machines. The source of training data set is a collection of top 20 News Group data. We are curious to learn that our proposed methods achieve better results than existing state of art solutions. Furthermore, CNN hides the complexity of the hierarchical model and requires less resources for prediction. We find there are much more of unexplored space for improvements and optimizations of CNN application for hierarchical text classification.
机译：本文介绍了适用于分层文本分类任务的卷积神经网络的应用。尽管已经显示CNN模型对于文本分类有效，但在层次结构的上下文中，并无预先探索。因此，需要更详细地评估CNN模型的实验。将我们进行的实验与已经存在的多种策略进行比较，这些策略使用线性回归和支持向量机。培训数据集的来源是前20名新闻组数据的集合。我们很奇怪地了解我们所提出的方法，而不是现有的艺术解决方案的效果更好。此外，CNN隐藏了分层模型的复杂性，并且需要更少的预测资源。我们发现有更多的未开发的空间，用于改进和优化用于分层文本分类的CNN应用程序。

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