Topic-aware cosine graph convolutional neural network for short text classification

Graph Convolutional Network (GCN) has been extensively studied in the task of short text classification (STC), utilizing global graphs that incorporate texts at different levels of granularity to learn text embeddings. However, the GCN-based methods only focus on the alignment between ground-truth labels and predicted labels, overlooking the geometric structure implicitly encoded by the graph. To address this limitation, we propose a novel GCN-based method that is entitled Topic-aware Cosine GCN (ToCo-GCN) for the STC. The ToCo-GCN defines and captures underlying geometric structures of short texts from different categories in the cosine space. Specifically, the ToCo-GCN regards the within-class and between-class geometric structures as constraint, aiming to learn both representative and discriminative short text representations. Moreover, to mitigate the inherent sparsity problem of short texts, the ToCo-GCN augment the text graph with latent topics. Experimental results on 8 STC datasets demonstrate that the ToCo-GCN is superior to state-of-the-art baselines in terms of Accuracy and Macro-F1 score. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.