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

Information distribution by electronic messages is a privileged means oftransmission for many businesses and individuals, often under the form ofplain-text tables. As their number grows, it becomes necessary to use analgorithm to extract text and numbers instead of a human. Usual methods arefocused on regular expressions or on a strict structure in the data, but arenot efficient when we have many variations, fuzzy structure or implicit labels.In this paper we introduce SC2T, a totally self-supervised model forconstructing vector representations of tokens in semi-structured messages byusing characters and context levels that address these issues. It can then beused for an unsupervised labeling of tokens, or be the basis for asemi-supervised information extraction system.

Putting Self-Supervised Token Embedding on the Tables