Label-Dividing Gated Graph Neural Network for Hierarchical Text Classification

Multi-label hierarchical text classification (MLHTC) is an essential yet challenging task of natural language processing (NLP). Existing methods lack attention to predicting sibling labels. In addition, methods based on graph convolutional networks (GCN) meet the problem of over-smoothing, which further deepens the difficulty of distinguishing sibling labels. In this paper, we propose a label-dividing gated graph neural network (LD-GGNN), which can better distinguish sibling labels and achieve adaptive interaction between text and labels. We optimize gated graph neural network (GGNN) to accurately capture structural features of label hierarchy and deeply explore label dependence. Stronger nonlinear characteristics of GGNN are used to solve the problem of over-smoothing. Furthermore, we propose a dynamic label dividing mechanism (DLDM), which can guide the model to distinguish sibling labels by introducing a dividing bias. Compared with previous works, LD-GGNN achieves significant and consistent improvements on both Micro-F1 and Macro-F1 score on multiple datasets.