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

Traditional Chinese Medicine (TCM) has accumulated a big amount of preciousresource in the long history of development. TCM prescriptions that consist ofTCM herbs are an important form of TCM treatment, which are similar to naturallanguage documents, but in a weakly ordered fashion. Directly adapting languagemodeling style methods to learn the embeddings of the herbs can be problematicas the herbs are not strictly in order, the herbs in the front of theprescription can be connected to the very last ones. In this paper, we proposeto represent TCM herbs with distributed representations via Prescription LevelLanguage Modeling (PLLM). In one of our experiments, the correlation betweenour calculated similarity between medicines and the judgment of professionalsachieves a Spearman score of 55.35 indicating a strong correlation, whichsurpasses human beginners (TCM related field bachelor student) by a big margin(over 10%).

Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models