E-commerce product pages on the web often present product specification data
in structured tabular blocks. Extraction of these product attribute-value
specifications has benefited applications like product catalogue curation,
search, question answering, and others. However, across different Websites,
there is a wide variety of HTML elements (like <table>, <ul>, <div>, <span>,
<dl> etc.) typically used to render these blocks that makes their automatic
extraction a challenge. Most of the current research has focused on extracting
product specifications from tables and lists and, therefore, suffers from
recall when applied to a large-scale extraction setting. In this paper, we
present a product specification extraction approach that goes beyond tables or
lists and generalizes across the diverse HTML elements used for rendering
specification blocks. Using a combination of hand-coded features and deep
learned spatial and token features, we first identify the specification blocks
on a product page. We then extract the product attribute-value pairs from these
blocks following an approach inspired by wrapper induction. We created a
labeled dataset of product specifications extracted from 14,111 diverse
specification blocks taken from a range of different product websites. Our
experiments show the efficacy of our approach compared to the current
specification extraction models and support our claim about its application to
large-scale product specification extraction.

利用深度学习和包裹感知算法结合手工设计的特征，我们提出了一个可一般化解析多种 HTML 元素的产品规格表述提取方法，并利用此方法从不同网站的 14,111 个规格表述块中创建了标注数据集，与目前的规格表述提取模型相比，我们的方法具有更高的准确性，且适用于大规模的产品规格提取任务。