Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Mustafa Jarrar, Mohammed Khalilia, Sana Ghanem


Abstract
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. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen’s Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning using the pre-trained AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available.
Anthology ID:
2022.lrec-1.387
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3626–3636
Language:
URL:
https://aclanthology.org/2022.lrec-1.387
DOI:
Bibkey:
Cite (ACL):
Mustafa Jarrar, Mohammed Khalilia, and Sana Ghanem. 2022. Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3626–3636, Marseille, France. European Language Resources Association.
Cite (Informal):
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT (Jarrar et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.lrec-1.387.pdf
Code
 SinaLab/ArabicNER
Data
NNE