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

Many joint entity relation extraction models setup two separated label spacesfor the two sub-tasks (i.e., entity detection and relation classification). Weargue that this setting may hinder the information interaction between entitiesand relations. In this work, we propose to eliminate the different treatment onthe two sub-tasks' label spaces. The input of our model is a table containingall word pairs from a sentence. Entities and relations are represented bysquares and rectangles in the table. We apply a unified classifier to predicteach cell's label, which unifies the learning of two sub-tasks. For testing, aneffective (yet fast) approximate decoder is proposed for finding squares andrectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC)show that, using only half the number of parameters, our model achievescompetitive accuracy with the best extractor, and is faster.

UniRE: A Unified Label Space for Entity Relation Extraction