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

Matching mentions of persons to the actual persons (the name disambiguationproblem) is central for several digital library applications. Scientists havebeen working on algorithms to create this matching for decades without findinga universal solution. One problem is that test collections for this problem areoften small and specific to a certain collection. In this work, we present anapproach that can create large test collections from historical metadata withminimal extra cost. We apply this approach to the DBLP collection to generatetwo freely available test collections. One collection focuses on the propertiesof defects and one on the evaluation of disambiguation algorithms.

Harnessing Historical Corrections to build Test Collections for Named Entity Disambiguation