no code implementations • EACL (WANLP) 2021 • Abeer Abuzayed, Hend Al-Khalifa
In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021).
no code implementations • OSACT (LREC) 2022 • Halah AlMazrua, Najla AlHazzani, Amaal AlDawod, Lama AlAwlaqi, Noura AlReshoudi, Hend Al-Khalifa, Luluh AlDhubayi
Thus, the AraBERT model achieved the most accurate result in detecting irony phrases in Saudi Dialect.
no code implementations • OSACT (LREC) 2022 • Hamdy Mubarak, Hend Al-Khalifa, Abdulmohsen Al-Thubaity
This paper provides an overview of the shard task on detecting offensive language, hate speech, and fine-grained hate speech at the fifth workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5).
no code implementations • COLING (WANLP) 2020 • Raghad Alshaalan, Hend Al-Khalifa
To conduct our experiments, we firstly built a new hate speech dataset that contains 9, 316 annotated tweets.
no code implementations • 18 Apr 2023 • Abeer Alessa, Hend Al-Khalifa
The system is designed to provide companionship and help reduce feelings of loneliness and social isolation.
1 code implementation • 5 Apr 2023 • Hend Al-Khalifa, Malak Mashaabi, Ghadi Al-Yahya, Raghad Alnashwan
This paper introduces the Saudi Privacy Policy Dataset, a diverse compilation of Arabic privacy policies from various sectors in Saudi Arabia, annotated according to the 10 principles of the Personal Data Protection Law (PDPL); the PDPL was established to be compatible with General Data Protection Regulation (GDPR); one of the most comprehensive data regulations worldwide.
no code implementations • 16 Dec 2022 • Malak Mashaabi, Areej Alotaibi, Hala Qudaih, Raghad Alnashwan, Hend Al-Khalifa
The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods.
no code implementations • 3 Nov 2022 • Mais Alheraki, Rawan Al-Matham, Hend Al-Khalifa
In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset.
no code implementations • 25 Nov 2020 • Kareem Darwish, Nizar Habash, Mourad Abbas, Hend Al-Khalifa, Huseein T. Al-Natsheh, Samhaa R. El-Beltagy, Houda Bouamor, Karim Bouzoubaa, Violetta Cavalli-Sforza, Wassim El-Hajj, Mustafa Jarrar, Hamdy Mubarak
The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design.
no code implementations • LREC 2020 • Hamdy Mubarak, Kareem Darwish, Walid Magdy, Tamer Elsayed, Hend Al-Khalifa
This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4).
no code implementations • 22 May 2018 • Nora Al-Twairesh, Hend Al-Khalifa, Abdulmalik Al-Salman, Yousef Al-Ohali
Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way).
no code implementations • LREC 2016 • Nora Al-Twairesh, Abeer Al-Dayel, Hend Al-Khalifa, Maha Al-Yahya, Sinaa Alageel, Nora Abanmy, Nouf Al-Shenaifi
This paper introduces MADAD, a general-purpose annotation tool for Arabic text with focus on readability annotation.