Mohamed Eldesouki | Hamad Bin Khalifa University
Hamad Bin Khalifa University


Mohamed Eldesouki

Research Associate
Qatar Computing Research Institute QCRI
Arabic Language Technologies

  • Phone44547781
  • Office locationOpen space, 2nd, South Building


I am a research associate at Qatar Computing Research Institute (QCRI), part of Qatar Foundation. My research focuses on various topics that are mostly related to human language technologies such as information retrieval, computational linguistics, information extraction, question answering, computational semantics, and speech processing. Furthermore, I conduct research in the fields of recommender systems and social computing. I earned my master's degree (MSc) from Cairo University. I gained practical experience working in the industry as an senior R&D engineer, which has sharpened my skills in using a vast spectrum of programming languages and technologies to build real-world large-scale projects and applications.


Research Interests

  • Natural Language Processing
  • Information Retrieval
  • Machine Learning


R&D Software Engineer

Taya IT

  • Research Assistant, ISSR

    Cairo University



Master of Science (MSc) in Computer science

Cairo University; Giza, Egypt

  • Bachelor's degree

    Cairo University; Giza, Egypt


Selected Publications

  • Younes Samih, Mohamed Eldesouki, Mohammed Attia,Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak and Laura Kallmeyer

    Learning from Relatives: Unified Dialectal Arabic Segmentation. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, 432-441.

  • Mohamed Eldesouki, Younes Samih, Ahmed Abdelali, Mohammed Attia, Hamdy Mubarak, Kareem Darwish and Kallmeyer Laura

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

  • Salvatore Romeo, Giovanni Da San Martino, Yonatan Belinkov, Alberto Barron-Cedeno, Mohamed Eldesouki, Kareem Darwish, Hamdy Mubarak, James Glass, and Alessandro Moschitti

    Language Processing and Learning Models for Community Question Answering in Arabic, Information Processing & Management (In Press), (

  • Mohamed Eldesouki, Fahim Dalvi, Hassan Sajjad, and Kareem Darwish

    QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features, Proc. of the 3rd Workshop on NLP for Similar Languages, Varieties and Dialects, (VarDial 3), Osaka, Japan, P. 221.