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 several techniques to tackle the mismatch in class distributionsbetween training and test data in the Contextual Emotion Detection task ofSemEval 2019, by extending the existing methods for class imbalance problem.Reducing the distance between the distribution of prediction and ground truth,they consistently show positive effects on the performance. Also we propose anovel neural architecture which utilizes representation of overall context aswell as of each utterance. The combination of the methods and the modelsachieved micro F1 score of about 0.766 on the final evaluation.

SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification