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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Arabic morphology encapsulates many valuable features such as word root.Arabic roots are being utilized for many tasks; the process of extracting aword root is referred to as stemming. Stemming is an essential part of mostNatural Language Processing tasks, especially for derivative languages such asArabic. However, stemming is faced with the problem of ambiguity, where two ormore roots could be extracted from the same word. On the other hand,distributional semantics is a powerful co-occurrence model. It captures themeaning of a word based on its context. In this paper, a distributionalsemantics model utilizing Smoothed Pointwise Mutual Information (SPMI) isconstructed to investigate its effectiveness on the stemming analysis task. Itshowed an accuracy of 81.5%, with a at least 9.4% improvement over otherstemmers.

CBAS: context based arabic stemmer