English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The number of web pages is growing at an exponential rate, accumulatingmassive amounts of data on the web. It is one of the key processes to classifywebpages in web information mining. Some classical methods are based onmanually building features of web pages and training classifiers based onmachine learning or deep learning. However, building features manually requiresspecific domain knowledge and usually takes a long time to validate thevalidity of features. Considering webpages generated by the combination of textand HTML Document Object Model(DOM) trees, we propose a representation andclassification method based on a pre-trained language model and graph neuralnetwork, named PLM-GNN. It is based on the joint encoding of text and HTML DOMtrees in the web pages. It performs well on the KI-04 and SWDE datasets and onpractical dataset AHS for the project of scholar's homepage crawling.

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