Summary of Wojoodner 2024: the Second Arabic Named Entity Recognition Shared Task, by Mustafa Jarrar et al.
WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task
by Mustafa Jarrar, Nagham Hamad, Mohammed Khalilia, Bashar Talafha, AbdelRahim Elmadany, Muhammad Abdul-Mageed
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent Arabic Named Entity Recognition (NER) shared task, WojoodNER-2024, has been presented, focusing on fine-grained NER tasks. The task provided participants with a new dataset called wojoodfine, annotated with subtypes of entities. The shared task consisted of three subtasks: flat fine-grained NER, nested fine-grained NER, and an open-track NER for the Israeli War on Gaza. A total of 43 unique teams registered, with five participating in the flat fine-grained subtask, two tackling the nested fine-grained subtask, and one participating 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%. This shared task highlights the importance of Arabic NER research and its applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WojoodNER-2024 is a big project that helps computers understand written Arabic language better. It’s like a contest where teams try to find specific words in texts, like names, locations, or organizations. The project gave teams a special set of rules and data to work with, and the best teams got high scores. This means that these teams can help us make computers smarter at understanding Arabic language. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
