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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose an end-to-end neural network to predict the geolocation of atweet. The network takes as input a number of raw Twitter metadata such as thetweet message and associated user account information. Our model is languageindependent, and despite minimal feature engineering, it is interpretable andcapable of learning location indicative words and timing patterns. Compared tostate-of-the-art systems, our model outperforms them by 2%-6%. Additionally, wepropose extensions to the model to compress representation learnt by thenetwork into binary codes. Experiments show that it produces compact codescompared to benchmark hashing algorithms. An implementation of the model isreleased publicly.

End-to-end Network for Twitter Geolocation Prediction and Hashing