1 code implementation • EACL (WANLP) 2021 • Haitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi, Wael Farhan, Bashar Talafha, Riham Badawi, Zyad Sober, Oday Al-Dweik, Abed Alhakim Freihat, Hussein Al-Natsheh
The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU).
no code implementations • WS 2019 • Bashar Talafha, Wael Farhan, Ahmed Altakrouri, Hussein Al-Natsheh
Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one.
no code implementations • WS 2019 • Ahmad Ragab, Haitham Seelawi, Mostafa Samir, Abdelrahman Mattar, Hesham Al-Bataineh, Mohammad Zaghloul, Ahmad Mustafa, Bashar Talafha, Abed Alhakim Freihat, Hussein Al-Natsheh
In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1).
1 code implementation • 31 Aug 2015 • Gilles Louppe, Hussein Al-Natsheh, Mateusz Susik, Eamonn Maguire
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms.