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

TL;DR

PLM-GNN is a novel webpage classification approach that combines pre-trained language models and graph neural networks to jointly encode webpage text and HTML DOM structures, improving classification accuracy.

Contribution

The paper introduces PLM-GNN, a new method that jointly encodes webpage text and HTML DOM trees using pre-trained language models and GNNs, addressing feature engineering challenges.

Findings

Performs well on KI-04 and SWDE datasets

Effective on practical scholar homepage crawling dataset

Outperforms traditional feature-based methods

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.