no code implementations • EACL (WANLP) 2021 • Fatemah Husain, Ozlem Uzuner
Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity.
no code implementations • 7 Feb 2022 • Fatemah Husain, Ozlem Uzuner
The problem of online offensive language limits the health and security of online users.
no code implementations • 9 Feb 2021 • Fatemah Husain, Ozlem Uzuner
In our study, we apply the principles of transfer learning cross multiple Arabic offensive language datasets to compare the effects on system performance.
no code implementations • 20 Jan 2021 • Fatemah Husain, Ozlem Uzuner
This paper adding more insights towards resources and datasets used in Arabic offensive language research.
no code implementations • SEMEVAL 2020 • Fatemah Husain, Jooyeon Lee, Sam Henry, Ozlem Uzuner
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.
no code implementations • 28 Jul 2020 • Fatemah Husain, Jooyeon Lee, Samuel Henry, Ozlem Uzuner
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.
no code implementations • 16 May 2020 • Fatemah Husain
Our study shows significant impact for applying ensemble machine learning approach over the single learner machine learning approach.
no code implementations • LREC 2020 • Fatemah Husain
This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text.