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

Document-level Relation Extraction (RE) is a more challenging task thansentence RE as it often requires reasoning over multiple sentences. Yet, humanannotators usually use a small number of sentences to identify the relationshipbetween a given entity pair. In this paper, we present an embarrassingly simplebut effective method to heuristically select evidence sentences fordocument-level RE, which can be easily combined with BiLSTM to achieve goodperformance on benchmark datasets, even better than fancy graph neural networkbased methods. We have released our code athttps://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction