Query classification plays a crucial role in e-commerce, where the goal is to assign user queries to appropriate categories within a hierarchical product category taxonomy. However, existing methods rely on a limited number of words from the category description and often neglect the hierarchical structure of the category tree, resulting in suboptimal category representations. To overcome these limitations, we propose a novel approach named hierarchical contrastive learning framework for query classification (HCL4QC), which leverages the hierarchical category tree structure to improve the performance of query classification. Specifically, HCL4QC is designed as a plugin module that consists of two innovative losses, namely local hierarchical contrastive loss (LHCL) and global hierarchical contrastive loss (GHCL). LHCL adjusts representations of categories according to their positional relationship in the hierarchical tree, while GHCL ensures the semantic consistency between the parent category and its child categories. Our proposed method can be adapted to any query classification tasks that involve a hierarchical category structure. We conduct experiments on two real-world datasets to demonstrate the superiority of our hierarchical contrastive learning. The results demonstrate significant improvements in the query classification task, particularly for long-tail categories with sparse supervised information.

The text classification methods heavily depend on machine learning algorithms with abstract mathematic metrics, which obstruct the direct observation and intuitive understanding of the text-specific classification. In this paper, we model a document as a Document-Classes-Topics top-down hierarchical structure. Furthermore, by running the document generation procedure, we can obtain each class's content share, which not only can be used to make the classification decision but also can provide a natural visualization approach for text classification. We implement this idea by a new tool named TC-DCA, which provides the visualization of text classification result, where the target document is expressed graphically as its content's allocation on every class. TC-DCA can also perform the drilling down operation to reveal the classification effect of each word of the document.