WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task
Mustafa Jarrar, Nagham Hamad, Mohammed Khalilia, Bashar Talafha,, AbdelRahim Elmadany, Muhammad Abdul-Mageed

TL;DR
WojoodNER-2024 is a shared task focusing on fine-grained Arabic NER, introducing a new dataset and evaluating multiple subtasks with high-performing teams achieving over 90% F-1 scores.
Contribution
This paper presents the second Arabic NER shared task with a new annotated dataset and multiple subtasks, advancing research in fine-grained Arabic NER.
Findings
High F-1 scores of 91% and 92% in flat and nested subtasks
43 teams registered, showing strong community interest
Open-Track achieved 73.7% F-1 score
Abstract
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 Arabic fine-grained NER dataset called wojoodfine, annotated with subtypes of entities. WojoodNER-2024 encompassed three subtasks: (i) Closed-Track Flat Fine-Grained NER, (ii) Closed-Track Nested Fine-Grained NER, and (iii) an Open-Track NER for the Israeli War on Gaza. A total of 43 unique teams registered for this shared task. Five teams participated in the Flat Fine-Grained Subtask, among which two teams tackled the Nested Fine-Grained Subtask and one team participated in the Open-Track NER Subtask. The winning teams achieved F-1 scores of 91% and 92% in the Flat Fine-Grained and Nested Fine-Grained Subtasks, respectively. The sole team in the Open-Track Subtask achieved an F-1 score of 73.7%.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text Readability and Simplification
MethodsFocus
