A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.Comment: Accepted by IEEE TKD

Arabic named entity recognition-a survey and analysis

As Arabic digital data has been increasing in abundance; the need for processing this information is growing. Named entity recognition (NER) is an information extraction technique that is vital to the processes of natural language processing (NLP). The ambiguous characteristics of the Arabic language make tasks related to NER and NLP very challenging. In addition to that, work related to Arabic NER is rather limited and under-studied. In this study, we survey previous works and methodologies and provide an analysis and discussion on the feature sets used, evaluation tools and advantages and disadvantages of each technique. Springer International Publishing Switzerland 2016.Scopu