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

Terms in diachronic text corpora may exhibit a high degree of semanticdynamics that is only partially captured by the common notion of semanticchange. The new measure of context volatility that we propose models the degreeby which terms change context in a text collection over time. The computationof context volatility for a word relies on the significance-values of itsco-occurrent terms and the corresponding co-occurrence ranks in sequential timespans. We define a baseline and present an efficient computational approach inorder to overcome problems related to computational issues in the datastructure. Results are evaluated both, on synthetic documents that are used tosimulate contextual changes, and a real example based on British newspapertexts.

Detecting and assessing contextual change in diachronic text documents using context volatility