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

Sentence-level relation extraction aims to identify the relation between twoentities for a given sentence. The existing works mostly focus on obtaining abetter entity representation and adopting a multi-label classifier for relationextraction. A major limitation of these works is that they ignore backgroundrelational knowledge and the interrelation between entity types and candidaterelations. In this work, we propose a new paradigm, Contrastive Learning withDescriptive Relation Prompts(CTL-DRP), to jointly consider entity information,relational knowledge and entity type restrictions. In particular, we introducean improved entity marker and descriptive relation prompts when generatingcontextual embedding, and utilize contrastive learning to rank the restrictedcandidate relations. The CTL-DRP obtains a competitive F1-score of 76.7% onTACRED. Furthermore, the new presented paradigm achieves F1-scores of 85.8% and91.6% on TACREV and Re-TACRED respectively, which are both the state-of-the-artperformance.

Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts