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

We compare the use of LSTM-based and CNN-based character-level wordembeddings in BiLSTM-CRF models to approach chemical and disease named entityrecognition (NER) tasks. Empirical results over the BioCreative V CDR corpusshow that the use of either type of character-level word embeddings inconjunction with the BiLSTM-CRF models leads to comparable state-of-the-artperformance. However, the models using CNN-based character-level wordembeddings have a computational performance advantage, increasing training timeover word-based models by 25% while the LSTM-based character-level wordembeddings more than double the required training time.

Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition