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

For named entity recognition (NER), bidirectional recurrent neural networksbecame the state-of-the-art technology in recent years. Competing approachesvary with respect to pre-trained word embeddings as well as models forcharacter embeddings to represent sequence information most effectively. ForNER in German language texts, these model variations have not been studiedextensively. We evaluate the performance of different word and characterembeddings on two standard German datasets and with a special focus onout-of-vocabulary words. With F-Scores above 82% for the GermEval'14 datasetand above 85% for the CoNLL'03 dataset, we achieve (near) state-of-the-artperformance for this task. We publish several pre-trained models wrapped into amicro-service based on Docker to allow for easy integration of German NER intoother applications via a JSON API.

microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF