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

Recognizing entities in texts is a central need in many information-seekingscenarios, and indeed, Named Entity Recognition (NER) is arguably one of themost successful examples of a widely adopted NLP task and corresponding NLPtechnology. Recent advances in large language models (LLMs) appear to provideeffective solutions (also) for NER tasks that were traditionally handled withdedicated models, often matching or surpassing the abilities of the dedicatedmodels. Should NER be considered a solved problem? We argue to the contrary:the capabilities provided by LLMs are not the end of NER research, but ratheran exciting beginning. They allow taking NER to the next level, tacklingincreasingly more useful, and increasingly more challenging, variants. Wepresent three variants of the NER task, together with a dataset to supportthem. The first is a move towards more fine-grained -- and intersectional --entity types. The second is a move towards zero-shot recognition and extractionof these fine-grained types based on entity-type labels. The third, and mostchallenging, is the move from the recognition setup to a novel retrieval setup,where the query is a zero-shot entity type, and the expected result is all thesentences from a large, pre-indexed corpus that contain entities of thesetypes, and their corresponding spans. We show that all of these are far frombeing solved. We provide a large, silver-annotated corpus of 4 millionparagraphs covering 500 entity types, to facilitate research towards all ofthese three goals.

NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval