Mowjaz Multi-Topic Labelling Task
Mowjaz is an Arabic topical content aggregation mobile application for news, sport, entertainment and other topics that users can follow. Mowjaz search engine and recommendation system is built on top of NLP/NLU machine learning APIs that distinguish it from any other News and entertainment applicat...
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| creator | Al-Ayyoub, Mahmoud Selawi, Haitham Zaghlol, Mohamed Al-Natsheh, Hussein Suileman, Samer Fadel, Ali Badawi, Riham Morsy, Ahmed Ibraheem Tuffaha Mohannad Aljarrah |
| description | Mowjaz is an Arabic topical content aggregation mobile application for news, sport, entertainment and other topics that users can follow. Mowjaz search engine and recommendation system is built on top of NLP/NLU machine learning APIs that distinguish it from any other News and entertainment applications available, mainly focusing on the users having the best experience and receiving content that is of their interest. One of Mowjaz’s top AI powered models is Topic Multi-Labelling, which is the focus of this shared task. This model is basically used to classify articles based on their topics. Additionally, the model predicts multiple topics in one article and is categorized to all possible topics that are present within its content. Mowjaz's topics are classified into ten categories and an article can be classified under as many topics as it covers. This model helps users get and display the most relevant news to their interests. The enhanced user experience that Mowjaz offers makes one news article be classified and shown under all the different topics that it holds. Mowjaz Multi-Topic Labelling Task (just.edu.jo) |
| doi_str_mv | 10.5281/zenodo.4554590 |
| format | Dataset |
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| identifier | DOI: 10.5281/zenodo.4554590 |
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| language | ara |
| recordid | cdi_datacite_primary_10_5281_zenodo_4554590 |
| source | DataCite |
| title | Mowjaz Multi-Topic Labelling Task |
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