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

Code-switching is a common phenomenon among people with diverse lingualbackground and is widely used on the internet for communication purposes. Inthis paper, we present a Recurrent Neural Network combined with the AttentionModel for Language Identification in Code-Switched Data in English and lowresource Roman Urdu. The attention model enables the architecture to learn theimportant features of the languages hence classifying the code switched data.We demonstrated our approach by comparing the results with state of the artmodels i.e. Hidden Markov Models, Conditional Random Field and BidirectionalLSTM. The models evaluation, using confusion matrix metrics, showed that theattention mechanism provides improved the precision and accuracy as compared tothe other models.

An Attention Based Neural Network for Code Switching Detection: English & Roman Urdu