WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task
Why this work is in the frame
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Bibliographic record
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 $F1$ 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 $F1$ score of $73.7%$.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it