A BERT-based Hierarchical Classification Model with Applications in Chinese Commodity Classification

• URL: http://arxiv.org/abs/2508.15800v1
• Date: Wed, 13 Aug 2025 16:10:47 GMT
• Title: A BERT-based Hierarchical Classification Model with Applications in Chinese Commodity Classification
• Authors: Kun Liu, Tuozhen Liu, Feifei Wang, Rui Pan,
• Abstract summary: We introduce a large-scale hierarchical dataset collected from the JD e-commerce platform (www.JD.com)<n>We also propose a novel hierarchical text classification approach based on the widely used Bidirectional Representations from Transformers (BERT)<n>Our HFT-BERT model demonstrates exceptional performance in categorizing longer short texts, such as books.
• Score: 12.186379198760733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing e-commerce platforms heavily rely on manual annotation for product categorization, which is inefficient and inconsistent. These platforms often employ a hierarchical structure for categorizing products; however, few studies have leveraged this hierarchical information for classification. Furthermore, studies that consider hierarchical information fail to account for similarities and differences across various hierarchical categories. Herein, we introduce a large-scale hierarchical dataset collected from the JD e-commerce platform (www.JD.com), comprising 1,011,450 products with titles and a three-level category structure. By making this dataset openly accessible, we provide a valuable resource for researchers and practitioners to advance research and applications associated with product categorization. Moreover, we propose a novel hierarchical text classification approach based on the widely used Bidirectional Encoder Representations from Transformers (BERT), called Hierarchical Fine-tuning BERT (HFT-BERT). HFT-BERT leverages the remarkable text feature extraction capabilities of BERT, achieving prediction performance comparable to those of existing methods on short texts. Notably, our HFT-BERT model demonstrates exceptional performance in categorizing longer short texts, such as books.

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