Name: Wojood
Description: Wojood consists of about 550K tokens (MSA and dialect) that are manually annotated with 21 entity types (e.g., person, organization, location, event, date, etc). It covers multiple domains and was annotated with nested entities. The corpus contains about 75K entities and 22.5% of which are nested.
Provider: Birzeit University
Paper Title: Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
Tasks: named entity recognition
Venue Name: nan
Authors: 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.
  