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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper proposes a ternary relation extraction method primarily based on rich syntactic information. We identify PROTEIN-ORGANISM-LOCATION relations in the text of biomedical articles. Different kernel functions are used with an SVM learner to integrate two sources of information from syntactic parse trees: (i) a large number of syntactic features that have been shown useful for Semantic Role Labeling (SRL) and applied here to the relation extraction task, and (ii) features from the entire parse tree using a tree kernel. Our experiments show that the use of rich syntactic features significantly outperforms shallow word-based features. The best accuracy is obtained by combining SRL features with tree kernels.

Exploiting rich syntactic information for relation extraction from biomedical articles