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 this work, we explore whether the recently demonstrated zero-shotabilities of the T0 model extend to Named Entity Recognition forout-of-distribution languages and time periods. Using a historical newspapercorpus in 3 languages as test-bed, we use prompts to extract possible namedentities. Our results show that a naive approach for prompt-based zero-shotmultilingual Named Entity Recognition is error-prone, but highlights thepotential of such an approach for historical languages lacking labeleddatasets. Moreover, we also find that T0-like models can be probed to predictthe publication date and language of a document, which could be very relevantfor the study of historical texts.

Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0