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 paper describes our system, which placed third in the Multilingual Track(subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in theChinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER MultilingualComplex Named Entity Recognition. Our system's key contributions are asfollows: 1) For multilingual NER tasks, we offer an unified framework withwhich one can easily execute single-language or multilingual NER tasks, 2) forlow-resource code-mixed NER task, one can easily enhance his or her datasetthrough implementing several simple data augmentation methods and 3) forChinese tasks, we propose a model that can capture Chinese lexical semantic,lexical border, and lexical graph structural information. Finally, our systemachieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9,respectively, during the testing phase.

Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task