Exploring the Impact of Annotation Schemes on Arabic Named Entity Recognition across General and Specific Domains

Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) that involves identifying and classifying entities into predefined categories. Despite its importance, the impact of annotation schemes and their interaction with domain types on NER performance, particularly for Arabic, remains underexplored. This study examines the influence of seven annotation schemes (IO, BIO, IOE, BIOES, BI, IE, and BIES) on arabic NER performance using the general-domain ANERCorp dataset and a domain-specific Moroccan legal corpus. Three models were evaluated: Logistic Regression (LR), Conditional Random Fields (CRF), and the transformer-based Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model. Results show that the impact of annotation schemes on performance is independent of domain type. Traditional Machine Learning (ML) models such as LR and CRF perform best with simpler annotation schemes like IO due to their computational efficiency and balanced precision-recall metrics. On the other hand, AraBERT excels with more complex schemes (BIOES, BIES), achieving superior performance in tasks requiring nuanced contextual understanding and intricate entity relationships, though at the cost of higher computational demands and execution time. These findings underscore the trade-offs between annotation scheme complexity and computational requirements, offering valuable insights for designing NER systems tailored to both general and domain-specific Arabic NLP applications.