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

We present an algorithm based on multi-layer transformers for identifyingAdverse Drug Reactions (ADR) in social media data. Our model relies on theproperties of the problem and the characteristics of contextual word embeddingsto extract two views from documents. Then a classifier is trained on each viewto label a set of unlabeled documents to be used as an initializer for a newclassifier in the other view. Finally, the initialized classifier in each viewis further trained using the initial training examples. We evaluated our modelin the largest publicly available ADR dataset. The experiments testify that ourmodel significantly outperforms the transformer-based models pretrained ondomain-specific data.

View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data