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

As the probability (and thus perplexity) of a text is calculated based on theproduct of the probabilities of individual tokens, it may happen that oneunlikely token significantly reduces the probability (i.e., increase theperplexity) of some otherwise highly probable input, while potentiallyrepresenting a simple typographical error. Also, given that perplexity is ascalar value that refers to the entire input, information about the probabilitydistribution within it is lost in the calculation (a relatively good text thathas one unlikely token and another text in which each token is equally likelythey can have the same perplexity value), especially for longer texts. As analternative to scalar perplexity this research proposes a simple algorithm usedto calculate vector values based on n-gram perplexities within the input. Suchrepresentations consider the previously mentioned aspects, and instead of aunique value, the relative perplexity of each text token is calculated, andthese values are combined into a single vector representing the input.

Text vectorization via transformer-based language models and n-gram perplexities