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

As of 2020 when the COVID-19 pandemic is full-blown on a global scale,people's need to have access to legitimate information regarding COVID-19 ismore urgent than ever, especially via online media where the abundance ofirrelevant information overshadows the more informative ones. In response tosuch, we proposed a model that, given an English tweet, automaticallyidentifies whether that tweet bears informative content regarding COVID-19 ornot. By ensembling different BERTweet model configurations, we have achievedcompetitive results that are only shy of those by top performing teams byroughly 1% in terms of F1 score on the informative class. In thepost-competition period, we have also experimented with various otherapproaches that potentially boost generalization to a new dataset.

Not-NUTs at W-NUT 2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets