Deep Structured Clustering of Short Text

Junxian Wu, Xiaojun Chen, Shaotian Cai, Yongqi Li, Huzi Wu

Abstract: Short text clustering is beneficial in many applications such as articles recommendations, user clustering and event exploration. Recent works of short text clustering boost the clustering results by improving the representation of short text with deep neural networks, such as CNN and autoencoder. However, existing short text deep clustering methods ignore the structure information of short texts. In this paper, we present a GCN-based clustering method for short text clustering, named as Deep Structured Clustering (DSC) method, to explore the relationships among short texts for representation learning. We first construct a $${\boldsymbol{k}}$$ -nn graph to capture the relationships among the short texts, and then jointly learn the short text representations and perform clustering with a dual self-supervised learning module. The experimental results demonstrate the superiority of our proposed method, and the ablation experimental results verify the effectiveness of the modules in our proposed method.