Commonsense knowledge powered heterogeneous graph attention networks for semi-supervised short text classification

In real-world scenarios, considerable human power and expert knowledge are required to label data. Therefore, solving short text classification problems in a semi-supervised manner is a good method. Existing graph-based semi-supervised short text classification models can achieve good classification performance. However, these models do not introduce some prior knowledge related to short texts, such as commonsense knowledge and concepts, from the knowledge bases to alleviate the data sparsity of short texts, which limits the improvement of classification performance. To overcome the above limitations, this paper addresses the problem of semi-supervised short text classification by proposing a novel model called the Commonsense Knowledge-Powered Heterogeneous Graph Attention Network (CSK-HGAT). This model mines the short text-related commonsense knowledge from the definitions corresponding to extracted short text keywords and obtains the short text conceptual information from the existing knowledge bases by conceptualizing the identified short text entities. Next, the embeddings are generated separately for short texts, commonsense knowledge, and concepts. Then, a heterogeneous information network (HIN) containing diverse knowledge is constructed by using the semantic relations among the short texts, commonsense knowledge and the concepts. Finally, this HIN is embedded into the heterogeneous graph attention network model (containing node-level and type-level attentions) to fully utilize the mined prior knowledge (i.e., commonsense knowledge and concepts) to achieve effective semi-supervised short text classification. Experimental results on four benchmark datasets show that the proposed CSK-HGAT model outperforms state-of-the-art semi-supervised short text classification models in terms of classification accuracy.