PLM-GNN: A Webpage Classification Method based on Joint Pre-trained Language Model and Graph Neural Network

• URL: http://arxiv.org/abs/2305.05378v1
• Date: Tue, 9 May 2023 12:19:10 GMT
• Title: PLM-GNN: A Webpage Classification Method based on Joint Pre-trained Language Model and Graph Neural Network
• Authors: Qiwei Lang, Jingbo Zhou, Haoyi Wang, Shiqi Lyu, Rui Zhang
• Abstract summary: We propose a representation and classification method based on a pre-trained language model and graph neural network, named PLM-GNN. It is based on the joint encoding of text and HTML DOM trees in the web pages. It performs well on the KI-04 and SWDE datasets and on practical dataset AHS for the project of scholar's homepage crawling.
• Score: 19.75890828376791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The number of web pages is growing at an exponential rate, accumulating massive amounts of data on the web. It is one of the key processes to classify webpages in web information mining. Some classical methods are based on manually building features of web pages and training classifiers based on machine learning or deep learning. However, building features manually requires specific domain knowledge and usually takes a long time to validate the validity of features. Considering webpages generated by the combination of text and HTML Document Object Model(DOM) trees, we propose a representation and classification method based on a pre-trained language model and graph neural network, named PLM-GNN. It is based on the joint encoding of text and HTML DOM trees in the web pages. It performs well on the KI-04 and SWDE datasets and on practical dataset AHS for the project of scholar's homepage crawling.

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