Arabic NLP

Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities …

This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and …

SALMA, the first Arabic sense-annotated corpus, consists of 34K tokens, which are all sense-annotated. The corpus is annotated using …

We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared Task. The primary focus of WojoodNER 2023 is on …

Offensive language detection has been well studied in many languages, but it is lagging behind in low-resource languages, such as …

Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the …

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. A nested named entity recognition (NER) model based on BERT was trained (F1-score 88.4%).

This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is …