41 citations · 47 across the 13 of their papers we have counts for
14 papers
Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition
Nagham Hamad, Mohammed Khalilia, Mustafa Jarrar
We introduce Konooz, a novel multi-dimensional corpus covering 16 Arabic dialects across 10 domains, resulting in 160 distinct corpora. The corpus comprises about 777k tokens, care…
Event-Arguments Extraction Corpus and Modeling using BERT for Arabic
Alaa Aljabari, Lina Duaibes, Mustafa Jarrar +1
Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the \hadath corpus (k tokens) as an…
ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task
Mohammed Khalilia, Sanad Malaysha, Reem Suwaileh +4
This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Me…
WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task
Mustafa Jarrar, Nagham Hamad, Mohammed Khalilia +3
We present WojoodNER-2024, the second Arabic Named Entity Recognition (NER) Shared Task. In WojoodNER-2024, we focus on fine-grained Arabic NER. We provided participants with a new…
AraFinNLP 2024: The First Arabic Financial NLP Shared Task
Sanad Malaysha, Mo El-Haj, Saad Ezzini +5
The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) share…
NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness
Sanad Malaysha, Mustafa Jarrar, Mohammed Khalilia
Semantic textual relatedness is a broader concept of semantic similarity. It measures the extent to which two chunks of text convey similar meaning or topics, or share related conc…
ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic
Mustafa Jarrar, Ahmet Birim, Mohammed Khalilia +2
This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and localized from the original English Banking77 d…
SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks
Mustafa Jarrar, Sanad Malaysha, Tymaa Hammouda +1
SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously…
WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task
Mustafa Jarrar, Muhammad Abdul-Mageed, Mohammed Khalilia +4
We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared Task. The primary focus of WojoodNER-2023 is on Arabic NER, offering novel NER datasets (i.e., Woj…
Arabic Fine-Grained Entity Recognition
Haneen Liqreina, Mustafa Jarrar, Mohammed Khalilia +2
Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level…
Context-Gloss Augmentation for Improving Arabic Target Sense Verification
Sanad Malaysha, Mustafa Jarrar, Mohammed Khalilia
Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consist…
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
Mustafa Jarrar, Mohammed Khalilia, Sana Ghanem
This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojo…
Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service
Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia +1
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service l…
Joint Entity Extraction and Assertion Detection for Clinical Text
Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia
Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the exist…