# A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends arXiv: https://arxiv.org/abs/2302.03512 ยท HTML: https://mlsanity.com/paper/2302.03512/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 62 of its 150 references that are in the map. - First posted on arXiv 7 Feb 2023. ## Builds on (most cited first, 30 of 62) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019) - [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017) - [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019) - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015) - [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021) - [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020) - [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018) - [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022) - [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019) - [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019) - [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019) - [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016) - [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021) - [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020) - [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021) - [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019) - [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020) - [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021) - [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020) - [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020) ## Built on by (most cited first, 8 of 8) - [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023) - [AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing](https://mlsanity.com/paper/2306.06800/) (2023) - [A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence](https://mlsanity.com/paper/2402.12928/) (2024) - [Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2406.01213/) (2024) - [RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU](https://mlsanity.com/paper/2409.05275/) (2024) - [ESNERA: Empirical and semantic named entity alignment for named entity dataset merging](https://mlsanity.com/paper/2508.06877/) (2025) - [Unsupervised Multidomain Approaches to Named Entity Recognition with Small Datasets](https://mlsanity.com/paper/2608.00984/) (2026) - [A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study](https://mlsanity.com/paper/2608.23421/) (2026) ## Abstract (arXiv) 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.