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This paper presents Wojood, a corpus for Arabic nested Named EntityRecognition (NER). Nested entities occur when one entity mention is embeddedinside another entity mention. Wojood consists of about 550K Modern StandardArabic (MSA) and dialect tokens that are manually annotated with 21 entitytypes including person, organization, location, event and date. Moreimportantly, the corpus is annotated with nested entities instead of the morecommon flat annotations. The data contains about 75K entities and 22.5% ofwhich are nested. The inter-annotator evaluation of the corpus demonstrated astrong agreement with Cohen's Kappa of 0.979 and an F1-score of 0.976. Tovalidate our data, we used the corpus to train a nested NER model based onmulti-task learning and AraBERT (Arabic BERT). The model achieved an overallmicro F1-score of 0.884. Our corpus, the annotation guidelines, the source codeand the pre-trained model are publicly available.

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT