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 clustering-based unsupervised relation discovery method has graduallybecome one of the important methods of open relation extraction (OpenRE).However, high-dimensional vectors can encode complex linguistic informationwhich leads to the problem that the derived clusters cannot explicitly alignwith the relational semantic classes. In this work, we propose arelation-oriented clustering model and use it to identify the novel relationsin the unlabeled data. Specifically, to enable the model to learn to clusterrelational data, our method leverages the readily available labeled data ofpre-defined relations to learn a relation-oriented representation. We minimizedistance between the instance with same relation by gathering the instancestowards their corresponding relation centroids to form a cluster structure, sothat the learned representation is cluster-friendly. To reduce the clusteringbias on predefined classes, we optimize the model by minimizing a jointobjective on both labeled and unlabeled data. Experimental results show thatour method reduces the error rate by 29.2% and 15.7%, on two datasetsrespectively, compared with current SOTA methods.

A Relation-Oriented Clustering Method for Open Relation Extraction