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

TL;DR

This survey reviews the evolution of Arabic Named Entity Recognition, highlighting recent advances with deep learning and pre-trained models, and discusses future research directions in this crucial NLP task.

Contribution

It provides a comprehensive overview of Arabic NER development, including traditional methods, recent deep learning approaches, and future challenges and trends.

Findings

Deep learning significantly improved Arabic NER performance.

Pre-trained language models further enhanced accuracy.

There is a notable gap between Arabic NER and other languages' NER methods.

Abstract

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.