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

After decades of massive digitisation, an unprecedented amount of historicaldocuments is available in digital format, along with their machine-readabletexts. While this represents a major step forward with respect to preservationand accessibility, it also opens up new opportunities in terms of contentmining and the next fundamental challenge is to develop appropriatetechnologies to efficiently search, retrieve and explore information from this'big data of the past'. Among semantic indexing opportunities, the recognitionand classification of named entities are in great demand among humanitiesscholars. Yet, named entity recognition (NER) systems are heavily challengedwith diverse, historical and noisy inputs. In this survey, we present the arrayof challenges posed by historical documents to NER, inventory existingresources, describe the main approaches deployed so far, and identify keypriorities for future developments.

Named Entity Recognition and Classification on Historical Documents: A Survey