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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Complex noun phrases are pervasive in biomedical texts, but are largely underexplored in entity discovery and information extraction. Such expressions often contain a mix of highly specific names (diseases, drugs, etc.) and common words such as “condition”, “degree”, “process”, etc. These words can have different semantic types depending on their context in noun phrases. In this paper, we address the task of classifying these common words onto fine-grained semantic types: for instance, “condition” can be typed as “symptom and finding” or “configuration and setting”. For information extraction tasks, it is crucial to consider common nouns only when they really carry biomedical meaning; hence the classifier must also detect the negative case when nouns are merely used in a generic, uninformative sense. Our solution harnesses a small number of labeled seeds and employs label propagation, a semisupervised learning method on graphs. Experiments on 50 frequent nouns show that our method computes semantic labels with a microaveraged accuracy of 91.34%.

Semantic Type Classification of Common Words in Biomedical Noun Phrases