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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

It is widely accepted that tagging text with semantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and introduce an unnecessary level of ambiguity. The noise produced by the redundant number of tags often overrides the advantage of semantic tagging. In this paper we propose an automatic method to select from WordNet a subset of domain-appropriate categories that effectively reduce the overambiguity of WordNet, and help at identifying and categorise relevant language patterns in a more compact way. The method is evaluated against a manually tagged corpus, SEMCOR.

Automatic selection of class labels from a thesaurus for an effective semantic tagging of corpora