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

We propose a new Named entity recognition (NER) method to effectively makeuse of the results of Part-of-speech (POS) tagging, Chinese word segmentation(CWS) and parsing while avoiding NER error caused by POS tagging error. Thispaper first uses Stanford natural language process (NLP) tool to annotatelarge-scale untagged data so as to reduce the dependence on the tagged data;then a new NLP model, g-BERT model, is designed to compress BidirectionalEncoder Representations from Transformers (BERT) model in order to reducecalculation quantity; finally, the model is evaluated based on Chinese NERdataset. The experimental results show that the calculation quantity in g-BERTmodel is reduced by 60% and performance improves by 2% with Test F1 to 96.5compared with that in BERT model.

A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis