用网页标记语言提升电商信息提取准确率
Markup Language Modeling for Web Document Understanding
- 基于网页DOM树结构微调MarkupLM模型
- 引入内部节点预测，精度达F1 0.805
- 适合需要高精度商品数据的电商平台
网页信息抽取（WIE）是众多电子商务系统的关键组成部分，支持客户分析与商品推荐等任务。本文聚焦于从不同规模的购物评论网站中提取详细信息，构建实时更新的商品数据库。我们对MarkupLM模型在多个购物网站采集的产品数据上进行微调，并提出改进版本MarkupLM++，将预测范围扩展至DOM树的内部节点。实验表明，使用更大更多样化的训练集能整体提升抽取准确率；引入内部节点有助于某些商品属性的识别，但略微降低整体性能。最终模型达到精确率0.906、召回率0.724、F1分数0.805。
原文摘要 · Abstract (English)
Web information extraction (WIE) is an important part of many e-commerce systems, supporting tasks like customer analysis and product recommendation. In this work, we look at the problem of building up-to-date product databases by extracting detailed information from shopping review websites. We fine-tuned MarkupLM on product data gathered from review sites of different sizes and then developed a variant we call MarkupLM++, which extends predictions to internal nodes of the DOM tree. Our experiments show that using larger and more diverse training sets improves extraction accuracy overall. We also find that including internal nodes helps with some product attributes, although it leads to a slight drop in overall performance. The final model reached a precision of 0.906, recall of 0.724, and an F1 score of 0.805.
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