用BERT改进中文商品分类，提升长文本识别效果。
A BERT-based Hierarchical Classification Model with Applications in Chinese Commodity Classification
- 基于BERT构建分层微调模型，利用层级结构信息
- 在101万商品数据上表现优于现有方法，尤其擅长长文本
- 适合电商、商品管理等需要自动分类的场景
现有电商平台依赖人工标注进行商品分类，效率低且不一致。虽然平台普遍采用三级分类结构，但很少有研究有效利用这一层级信息。此外，现有方法未能充分考虑不同层级类别间的相似性与差异性。为此，我们从京东平台收集并公开了一个大规模分层数据集，包含1,011,450个商品标题及三级类别结构，为相关研究提供宝贵资源。同时，提出一种基于BERT的新型分层文本分类方法——分层微调BERT（HFT-BERT）。该模型充分利用BERT强大的文本表征能力，在短文本分类任务中表现接近现有方法。尤为突出的是，其在较长短文本（如书籍）分类任务中表现出色。
原文摘要 · Abstract (English)
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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