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/

Zendy Plus

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

Zendy Tools

Zendy Open

Crowdsourcing has attracted much attention for its convenience to collectlabels from non-expert workers instead of experts. However, due to the highlevel of noise from the non-experts, an aggregation model that learns the truelabel by incorporating the source credibility is required. In this paper, wepropose a novel framework based on graph neural networks for aggregating crowdlabels. We construct a heterogeneous graph between workers and tasks and derivea new graph neural network to learn the representations of nodes and the truelabels. Besides, we exploit the unknown latent interaction between the sametype of nodes (workers or tasks) by adding a homogeneous attention layer in thegraph neural networks. Experimental results on 13 real-world datasets showsuperior performance over state-of-the-art models.

Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing