  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.
