Hypertext Entity Extraction in Webpage
Yifei Yang Tianqiao Liu Bo Shao Hai Zhao Linjun Shou Ming Gong Daxin Jiang
Webpage entity extraction is a fundamental natural language processing task
in both research and applications. Nowadays, the majority of webpage entity
extraction models are trained on structured datasets which strive to retain
textual content and its structure information. However, existing datasets all
overlook the rich hypertext features (e.g., font color, font size) which show
their effectiveness in previous works. To this end, we first collect a
\textbf{H}ypertext \textbf{E}ntity \textbf{E}xtraction \textbf{D}ataset
(\textit{HEED}) from the e-commerce domains, scraping both the text and the
corresponding explicit hypertext features with high-quality manual entity
annotations. Furthermore, we present the \textbf{Mo}E-based \textbf{E}ntity
\textbf{E}xtraction \textbf{F}ramework (\textit{MoEEF}), which efficiently
integrates multiple features to enhance model performance by Mixture of Experts
and outperforms strong baselines, including the state-of-the-art small-scale
models and GPT-3.5-turbo. Moreover, the effectiveness of hypertext features in
\textit{HEED} and several model components in \textit{MoEEF} are analyzed.