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

This article describes Amobee's participation in "HatEval: Multilingualdetection of hate speech against immigrants and women in Twitter" (task 5) and"OffensEval: Identifying and Categorizing Offensive Language in Social Media"(task 6). The goal of task 5 was to detect hate speech targeted to women andimmigrants. The goal of task 6 was to identify and categorized offensivelanguage in social media, and identify offense target. We present a novel typeof convolutional neural network called "Multiple Choice CNN" (MC-CNN) that weused over our newly developed contextual embedding, Rozental et al. (2019). Forboth tasks we used this architecture and achieved 4th place out of 69participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place(out of 75) in Sub-task B - automatic categorization of offense types (ourmodel reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and Crespectively in task 6).

Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding