# A Panoramic Survey of Natural Language Processing in the Arab World

arXiv: https://arxiv.org/abs/2011.12631 · HTML: https://mlsanity.com/paper/2011.12631/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 30 of 45)

- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Masader: Metadata Sourcing for Arabic Text and Speech Data Resources](https://mlsanity.com/paper/2110.06744/) (2021)
- [Curras + Baladi: Towards a Levantine Corpus](https://mlsanity.com/paper/2205.09692/) (2022)
- [ArabicaQA: A Comprehensive Dataset for Arabic Question Answering](https://mlsanity.com/paper/2403.17848/) (2024)
- [DziriBERT: a Pre-trained Language Model for the Algerian Dialect](https://mlsanity.com/paper/2109.12346/) (2021)
- [ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD](https://mlsanity.com/paper/2205.09685/) (2022)
- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [Nabra: Syrian Arabic Dialects with Morphological Annotations](https://mlsanity.com/paper/2310.17315/) (2023)
- [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://mlsanity.com/paper/2211.16807/) (2022)
- [A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms](https://mlsanity.com/paper/2302.02232/) (2023)
- [SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks](https://mlsanity.com/paper/2310.19029/) (2023)
- [Lisan: Yemeni, Iraqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations](https://mlsanity.com/paper/2212.06468/) (2022)
- [ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic](https://mlsanity.com/paper/2310.19034/) (2023)
- [Arabic Fine-Grained Entity Recognition](https://mlsanity.com/paper/2310.17333/) (2023)
- [The FIGNEWS Shared Task on News Media Narratives](https://mlsanity.com/paper/2407.18147/) (2024)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [Context-Gloss Augmentation for Improving Arabic Target Sense Verification](https://mlsanity.com/paper/2302.03126/) (2023)
- [Offensive Hebrew Corpus and Detection using BERT](https://mlsanity.com/paper/2309.02724/) (2023)
- [WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task](https://mlsanity.com/paper/2310.16153/) (2023)
- [WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task](https://mlsanity.com/paper/2407.09936/) (2024)
- [Similarities between Arabic Dialects: Investigating Geographical Proximity](https://mlsanity.com/paper/2105.04221/) (2021)
- [Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic](https://mlsanity.com/paper/2205.10412/) (2022)
- [Qabas: An Open-Source Arabic Lexicographic Database](https://mlsanity.com/paper/2406.06598/) (2024)
- [Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propaganda](https://mlsanity.com/paper/2407.09327/) (2024)
- [Exploring Retrieval Augmented Generation in Arabic](https://mlsanity.com/paper/2408.07425/) (2024)
- [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025)
- [NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness](https://mlsanity.com/paper/2405.00659/) (2024)
- [Event-Arguments Extraction Corpus and Modeling using BERT for Arabic](https://mlsanity.com/paper/2407.21153/) (2024)
- [Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components](https://mlsanity.com/paper/2506.06339/) (2025)

## Abstract (arXiv)

The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design. This distinguishes natural languages such as Arabic and Japanese from artificially constructed languages such as Esperanto or Python. Natural language processing (NLP) is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc. NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others. Some of the earliest AI applications were in NLP (e.g., machine translation); and the last decade (2010-2020) in particular has witnessed an incredible increase in quality, matched with a rise in public awareness, use, and expectations of what may have seemed like science fiction in the past. NLP researchers pride themselves on developing language independent models and tools that can be applied to all human languages, e.g. machine translation systems can be built for a variety of languages using the same basic mechanisms and models. However, the reality is that some languages do get more attention (e.g., English and Chinese) than others (e.g., Hindi and Swahili). Arabic, the primary language of the Arab world and the religious language of millions of non-Arab Muslims is somewhere in the middle of this continuum. Though Arabic NLP has many challenges, it has seen many successes and developments. Next we discuss Arabic's main challenges as a necessary background, and we present a brief history of Arabic NLP. We then survey a number of its research areas, and close with a critical discussion of the future of Arabic NLP.