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

In this paper, we describe our participation in SemEval-2019 Task 3: EmoContext - A Shared Task on Contextual Emotion Detection in Text. We propose a three layer model with a generic, multi-purpose approach that without any task specific optimizations achieve competitive results (f1 score of 0.7096) in the EmoContext task. We describe our development strategy in detail along with an exposition of our results.

GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach