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/

Zendy Plus

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

Zendy Tools

Zendy Open

Code-switching (CS) is a common linguistic phenomenon exhibited bymultilingual individuals, where they tend to alternate between languages withinone single conversation. CS is a complex phenomenon that not only encompasseslinguistic challenges, but also contains a great deal of complexity in terms ofits dynamic behaviour across speakers. Given that the factors giving rise to CSvary from one country to the other, as well as from one person to the other, CSis found to be a speaker-dependant behaviour, where the frequency by which theforeign language is embedded differs across speakers. While several researchershave looked into predicting CS behaviour from a linguistic point of view,research is still lacking in the task of predicting user CS behaviour fromsociological and psychological perspectives. We provide an empirical userstudy, where we investigate the correlations between users' CS levels andcharacter traits. We conduct interviews with bilinguals and gather informationon their profiles, including their demographics, personality traits, andtraveling experiences. We then use machine learning (ML) to predict users' CSlevels based on their profiles, where we identify the main influential factorsin the modeling process. We experiment with both classification as well asregression tasks. Our results show that the CS behaviour is affected by therelation between speakers, travel experiences as well as Neuroticism andExtraversion personality traits.

The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles