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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we propose methods to take into account the disagreement between crowd annotators as well as their skills for weighting instances in learning algorithms. The latter can thus better deal with noise in the annotation and produce higher accuracy. We created two passage reranking datasets: one with crowdsource platform, and the second with an expert who completely revised the crowd annotation. Our experiments show that our weighting approach reduces noise improving passage reranking up to 1.47% and 1.85% on MRR and P@1, respectively.

Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data