Hypertext Entity Extraction in Webpage

• URL: http://arxiv.org/abs/2403.01698v1
• Date: Mon, 4 Mar 2024 03:21:40 GMT
• Title: Hypertext Entity Extraction in Webpage
• Authors: Yifei Yang, Tianqiao Liu, Bo Shao, Hai Zhao, Linjun Shou, Ming Gong, Daxin Jiang
• Abstract summary: We introduce a textbfMoE-based textbfEntity textbfExtraction textbfFramework (textitMoEEF), which integrates multiple features to enhance model performance. We also analyze the effectiveness of hypertext features in textitHEED and several model components in textitMoEEF.
• Score: 112.56734676713721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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