Zixuan Wu1, Ye Wang1,*, Lifeng Shen2, Feng Hu1, Hong Yu1,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581
Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >
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