Dual-view graph convolutional network for multi-label text classification

Multi-label text classification refers to assigning multiple relevant category labels to each text, which has been widely applied in the real world. To enhance the performance of multi-label text classification, most existing methods only focus on optimizing document and label representations, assuming accurate label-document similarity is crucial. However, whether the potential relevance between labels and if the problem of the long-tail distribution of labels could be solved are also key factors affecting the performance of multi-label classification. To this end, we propose a multi-label text classification model called DV-MLTC, which is based on a dual-view graph convolutional network to predict multiple labels for text. Specifically, we utilize graph convolutional neural networks to explore the potential correlation between labels in both the global and local views. First, we capture the global consistency of labels on the global label graph based on existing statistical information and generate label paths through a random walk algorithm to reconstruct the label graph. Then, to capture relationships between low-frequency co-occurring labels on the reconstructed graph, we guide the generation of reasonable co-occurring label pairs within the local neighborhood by utilizing the local consistency of labels, which also helps alleviate the long-tail distribution of labels. Finally, we integrate the global and local consistency of labels to address the problem of highly skewed distribution caused by incomplete label co-occurrence patterns in the label co-occurrence graph. The Evaluation shows that our proposed model achieves competitive results compared to existing state-of-the-art methods. Moreover, our model achieves a better balance between efficiency and performance.