HTV-GCN༚A Heterogeneous Three-View Graph Convolution Network for Multi-Label Text Classification

Abstract

Multi-Label Text Classification (MLTC) is a crucial task in Natural Language Processing (NLP) that involves assigning multiple labels to a given text. It has been extensively applied in various domains [1]. Most existing studies focus heavily on the manually annotated labels, while largely overlooking the complex interactions between labels and text. In this paper, a novel Heterogeneous Three-View Graph Convolution Network (HTV-GCN) is proposed, which combines a group-wise smooth contrastive mechanism with three heterogeneous graphs: global, local, and text-lemma. The global label graph serves to enrich the knowledge and conceptual structure of high-frequency labels, and the local label graph focuses on relationships between document-specific and long-tail labels. The text-lemma graph is designed to capture fine-grained word-level information. The proposed three-view framework significantly improves the expressive ability of text-label alignment. In addition, a novel contrastive mechanism is designed to enhance the discriminative strength between global and local label graphs. Comprehensive experiments conducted on benchmark datasets show that the proposed scheme consistently outperforms state-of-the-art baselines under various evaluation metrics, and ablations confirm the effectiveness of its contrastive mechanisms and multi-graph fusion.