论文标题
ME-GCN：半监视文本分类的多维边缘图形卷积网络
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
论文作者
论文摘要
与顺序学习模型相比，基于图的神经网络在捕获全球信息方面具有出色的能力，并已用于半监督学习任务。大多数图形卷积网络都是具有单维边缘功能的设计，并且未能利用有关图形的丰富边缘信息。本文介绍了半监督文本分类的ME-GCN（多维边缘增强图卷积网络）。首先构建了整个语料库的文本图，以描述单词到单词，文档文档和文字对象的无向和多维关系。该图是用训练语料库训练的多维单词和文档节点表示的初始化的，并且关系根据这些单词/文档节点的距离表示。然后，使用ME-GCN训练生成的图，该图将边缘特征视为多流信号，并且每个流执行单独的图形卷积操作。我们的ME-GCN可以整合整个文本语料库的丰富图形信息源。结果表明，我们提出的模型在八个基准数据集中大大优于最先进的方法。
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks. Most Graph Convolutional Networks are designed with the single-dimensional edge feature and failed to utilise the rich edge information about graphs. This paper introduces the ME-GCN (Multi-dimensional Edge-enhanced Graph Convolutional Networks) for semi-supervised text classification. A text graph for an entire corpus is firstly constructed to describe the undirected and multi-dimensional relationship of word-to-word, document-document, and word-to-document. The graph is initialised with corpus-trained multi-dimensional word and document node representation, and the relations are represented according to the distance of those words/documents nodes. Then, the generated graph is trained with ME-GCN, which considers the edge features as multi-stream signals, and each stream performs a separate graph convolutional operation. Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus. The results have demonstrated that our proposed model has significantly outperformed the state-of-the-art methods across eight benchmark datasets.
