基于知识增强的图卷积神经网络的文本分类

针对文本分类问题，提出新的基于知识增强的图卷积神经网络（KEGCN）分类模型. KEGCN模型在整个文本集上构建了一个包含单词节点、文档节点、外部实体节点的文本图，不同类型节点之间使用不同的相似性计算方法；在文本图构建完成后将其输入到2层图卷积网络中学习节点的表示并进行分类. KEGCN模型引入外部知识进行构图，捕获长距离不连续的全局语义信息，是第1个将知识信息引入图卷积网络进行分类任务的工作. 在4个大规模真实数据集20NG、OHSUMED、R52、R8上进行文本分类实验，结果表明，KEGCN模型的分类准确率优于所有的基线模型. 将知识信息融入图卷积神经网络有利于学习到更精准的文本表示，提高文本分类的准确率.

Knowledge-enhanced graph convolutional neural networks for text classification

A new knowledge-enhanced graph convolutional neural network (KEGCN) classification model was proposed aiming at the problem of text classification. In the KEGCN model, firstly a text graph containing word nodes, document nodes, and external entity nodes was constructed on the entire text set. Different similarity calculation methods were used between different types of nodes. After the text graph was constructed, it was input into the two-layer graph convolutional network to learn the representation of the node and classified. The KEGCN model introduced external knowledge to compose the graph, and captured the long-distance discontinuous global semantic information, and was the first work to introduce knowledge information into the graph convolution network for classification tasks. Text classification experiments were conducted on four large-scale real data sets, 20NG, OHSUMED, R52 and R8, and results showed that the classification accuracy of the KEGCN network model was better than that of all baseline models. Results show that integrating knowledge information into the graph convolutional neural network is conducive to learning more accurate text representations and improving the accuracy of text classification.