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.

网页实体抽取是一项基本的自然语言处理任务，在研究和应用中都非常重要。本文首先从电子商务领域采集了一个高质量的	extit{HEED}数据集，其中包含了文本和相应的显式超文本特征以及手动标注的实体注释；其次，提出了一种基于混合专家的	extit{MoEEF}实体抽取框架，通过有效地综合多个特征来提高模型性能，优于强基线模型及GPT-3.5-turbo；最后，分析了	extit{HEED}中的超文本特征以及	extit{MoEEF}中的几个模型组件的有效性。

网页中的超文本实体抽取