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

Discourse relations bind smaller linguistic elements into coherent texts.However, automatically identifying discourse relations is difficult, because itrequires understanding the semantics of the linked sentences. A more subtlechallenge is that it is not enough to represent the meaning of each sentence ofa discourse relation, because the relation may depend on links betweenlower-level elements, such as entity mentions. Our solution computesdistributional meaning representations by composition up the syntactic parsetree. A key difference from previous work on compositional distributionalsemantics is that we also compute representations for entity mentions, using anovel downward compositional pass. Discourse relations are predicted not onlyfrom the distributional representations of the sentences, but also of theircoreferent entity mentions. The resulting system obtains substantialimprovements over the previous state-of-the-art in predicting implicitdiscourse relations in the Penn Discourse Treebank.

Entity-Augmented Distributional Semantics for Discourse Relations