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

The massive scale and growth of textual biomedical data have made itsindexing and classification increasingly important. However, existing researchon this topic mainly utilized convolutional and recurrent neural networks,which generally achieve inferior performance than the novel transformers. Onthe other hand, systems that apply transformers only focus on the targetdocuments, overlooking the rich semantic information that label descriptionscontain. To address this gap, we develop a transformer-based biomedical textclassifier that considers label information. The system achieves this with alabel attention module incorporated into the fine-tuning process of pretrainedlanguage models (PTMs). Our results on two public medical datasets show thatthe proposed fine-tuning scheme outperforms the vanilla PTMs andstate-of-the-art models.

Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification