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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Large scale pre-training models have been widely used in named entityrecognition (NER) tasks. However, model ensemble through parameter averaging orvoting can not give full play to the differentiation advantages of differentmodels, especially in the open domain. This paper describes our NER system inthe SemEval 2022 task11: MultiCoNER. We proposed an effective system toadaptively ensemble pre-trained language models by a Transformer layer. Byassigning different weights to each model for different inputs, we adopted theTransformer layer to integrate the advantages of diverse models effectively.Experimental results show that our method achieves superior performances inFarsi and Dutch.

SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models