Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. Although more complex models tend to achieve better performance, research highly depends on the computing power of the device used. In this article, we propose TENT (https://github.com/Daisean/TENT) to obtain better text classification performance and reduce the reliance on computing power. Specifically, we first establish a dependency analysis graph for each text and then convert each graph into its corresponding encoding tree. The representation of the entire graph is obtained by updating the representation of the non-leaf nodes in the encoding tree. Experimental results show that our method outperforms other baselines on several datasets while having a simple structure and few parameters.

翻译：文本分类是自然语言处理(NLP)的一项主要任务。最近,图形神经网络(GNNs)迅速发展,并应用于文本分类任务。虽然较复杂的模型往往能够取得更好的性能,但研究高度取决于所用设备的计算能力。在本条中,我们建议TENT(https://github.com/Disean/TENT)取得更好的文本分类性能并减少对计算能力的依赖。具体地说,我们首先为每个文本建立一个依赖性分析图,然后将每个图表转换成相应的编码树。通过更新编码树中非叶节点的表示方式,获得了整个图表的表述。实验结果显示,我们的方法在结构简单和参数不多的情况下,优于几个数据集上的其他基线。