PLM-GNN: A Webpage Classification Method based on Joint Pre-trained
  Language Model and Graph Neural Network
Qiwei Lang Jingbo Zhou Haoyi Wang Shiqi Lyu Rui Zhang
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.