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20172025
most citedPre-Training BERT on Arabic Tweets: Practical Considerations

85 citations · 126 across the 6 of their papers we have counts for

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cs.CL20223 cited

Probing for Constituency Structure in Neural Language Models

David Arps, Younes Samih, Laura Kallmeyer +1

In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as re…

cs.CL202185 cited

Pre-Training BERT on Arabic Tweets: Practical Considerations

Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak +2

Pretraining Bidirectional Encoder Representations from Transformers (BERT) for downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that differ in the size of the…

cs.CL202025 cited

Arabic Dialect Identification in the Wild

Ahmed Abdelali, Hamdy Mubarak, Younes Samih +2

We present QADI, an automatically collected dataset of tweets belonging to a wide range of country-level Arabic dialects -covering 18 different countries in the Middle East and Nor…

cs.CL2020

Arabic Offensive Language on Twitter: Analysis and Experiments

Hamdy Mubarak, Ammar Rashed, Kareem Darwish +2

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arab…

cs.CL2018

Diacritization of Maghrebi Arabic Sub-Dialects

Ahmed Abdelali, Mohammed Attia, Younes Samih +2

Diacritization process attempt to restore the short vowels in Arabic written text; which typically are omitted. This process is essential for applications such as Text-to-Speech (T…

cs.CL201711 cited

Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM

Mohamed Eldesouki, Younes Samih, Ahmed Abdelali +4

Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval. Segmentation entails breaking words into their consti…