Graph Convolutional Network Based on Multi-Head Pooling for Short Text Classification

Abstract

The short text, sparse features, and the lack of training data, etc. are still the key bottlenecks that restrict the successful application of traditional text classification methods. To address these problems, we propose a Multi-head-Pooling-based Graph Convolutional Network (MP-GCN) for semi-supervised short text classification, and introduce its three architectures, which focus on the node representation learning of 1-order, 12-order of isomorphic graphs, and 1-order of heterogeneous graphs, respectively. It only focuses on the structural information of the text graph and does not need pre-training word embedding as the initial node feature. A graph pooling based on self-attention is introduced to evaluate and select important nodes, and the multi-head method is used to provide multiple representation subspaces for pooling without adding trainable parameters. Experimental results demonstrated that, without using pre-training embedding, MP-GCN outperforms state-of-the-art models across five benchmark datasets.