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

In Targeted Entity Disambiguation setting, we take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of “target entity”. For example, given the names of IT companies, including Apple, we determine Apple in a mention denotes an IT company or not. Prior work proposed a graph based model. This model ranks all candidate mentions based on scores which denote the degree of relevancy to target entities. Furthermore, this graph based model could utilize reference pages of target entities. However, human annotators must select reference pages in advance. We propose an automatic method that can select reference pages. We formalize the selection problem of reference pages as an Integer Linear Programming problem. We show that our model works as well as the prior work that manually selected reference pages.

Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation