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

To study social, economic, and historical questions, researchers in thesocial sciences and humanities have started to use increasingly largeunstructured textual datasets. While recent advances in NLP provide many toolsto efficiently process such data, most existing approaches rely on genericsolutions whose performance and suitability for domain-specific tasks is notwell understood. This work presents an attempt to bridge this domain gap byexploring the use of modern Entity Linking approaches for the enrichment ofmuseum collection data. We collect a dataset comprising of more than 1700 textsannotated with 7,510 mention-entity pairs, evaluate some off-the-shelfsolutions in detail using this dataset and finally fine-tune a recentend-to-end EL model on this data. We show that our fine-tuned modelsignificantly outperforms other approaches currently available in this domainand present a proof-of-concept use case of this model. We release our datasetand our best model.

Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections