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

Named Entity Recognition seeks to extract substrings within a text that namereal-world objects and to determine their type (for example, whether they referto persons or organizations). In this survey, we first present an overview ofrecent popular approaches, but we also look at graph- and transformer- basedmethods including Large Language Models (LLMs) that have not had much coveragein other surveys. Second, we focus on methods designed for datasets with scarceannotations. Third, we evaluate the performance of the main NER implementationson a variety of datasets with differing characteristics (as regards theirdomain, their size, and their number of classes). We thus provide a deepcomparison of algorithms that are never considered together. Our experimentsshed some light on how the characteristics of datasets affect the behavior ofthe methods that we compare.

A survey on recent advances in named entity recognition