---
title: "A Survey on Arabic Named Entity Recognition — Qu 2023"
description: "This survey reviews how named entity recognition for Arabic has developed and why it is harder than English NER."
authors: ["Xiaoye Qu", "Yingjie Gu", "Qingrong Xia", "Zechang Li", "Zhefeng Wang", "Baoxing Huai"]
year: 2023
venue: "arXiv:2302.03512"
source: "https://arxiv.org/abs/2302.03512"
doi: "10.48550/arxiv.2302.03512"
arxiv: "2302.03512"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qu-2023-survey-arabic-named-entity-recognition/"
---
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# A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

**Authors:** Xiaoye Qu, Yingjie Gu, Qingrong Xia, Zechang Li, [Zhefeng Wang](https://md.research.elamur.ai/free/en/authors/zhefeng-wang/), [Baoxing Huai](https://md.research.elamur.ai/free/en/authors/baoxing-huai/) · **Year:** 2023 · **Venue:** arXiv:2302.03512 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2302.03512) · [PDF](https://arxiv.org/pdf/2302.03512) · [DOI 10.48550/arxiv.2302.03512](https://doi.org/10.48550/arxiv.2302.03512) · [arXiv 2302.03512](https://arxiv.org/abs/2302.03512) · [OpenAlex](https://openalex.org/W4385656585)

**Citations:** 45 citations (openalex-snapshot · 2026-10-14) · [19](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
This survey reviews how named entity recognition for Arabic has developed and why it is harder than English NER. Arabic has no capital letters to signal names, and its words carry attached prefixes and suffixes. A single word can be spelled several ways, and because short vowels are usually omitted, one written form can stand for several words. The authors describe the main varieties of Arabic: Classical, Modern Standard and dialectal. They also catalogue the available resources and trace the methods in order: hand-written rules, feature-based machine learning, neural encoders (mostly Bi-LSTM with a CRF decoder) and pre-trained Arabic transformers. Earlier papers trained and tested on different data splits, so their scores could not be compared directly. To fix this, the authors gather reported results on ANERCorp and AQMAR in one place. They close by contrasting Arabic NER with work in other languages and listing open directions: fine-grained and nested entities, low-resource and distantly supervised training, and cross-lingual transfer. The survey matters because previous reviews stopped before modern deep learning and pre-trained models.

## Key points
- Milestones in the field:
  - TagArab (1998) used rule-based pattern matching.
  - ANERsys (2007) introduced the ANERcorp dataset, and a Maximum Entropy model the same year was the first machine-learning approach.
  - A DNN followed in 2016 and BiLSTM-CRF in 2019.
  - AraBERT (2020) was the first pre-trained Arabic model used for NER.
  - MARBERT (2021) covers both MSA and dialects.
  - The JABER family (JABER, SABER, Char-JABER) appeared in 2022.
- In 2020 the CAMeL Lab reorganized and relabeled ANERcorp.
- Four core difficulties: no capitalization, agglutination (entities glued to prepositions and conjunctions), spelling variants (many-to-one) and missing diacritics (one-to-many).
- NER output can take two forms: a list of (start, end, type) tuples, or per-token BIO labels such as B-PER, I-PER and O.
- Table 1 compares eight earlier surveys and reports. One of the reports is the winning system of a Topcoder.com Arabic NER challenge.

## Interesting ideas
- Results from different papers become comparable only when gathered on the same benchmark splits, so the survey re-tabulates them on ANERCorp and AQMAR rather than trusting headline numbers. — For Mova (learn): Keep a fixed-split scoreboard for every parser and frame model, and refuse cross-paper or cross-run comparisons whose data splits differ.
- Entities can be stored either as spans with a type or as per-token BIO tags, and each form suits different downstream needs. — For Mova (world): Store entities in the text world as typed spans tied to UD subtrees, and derive BIO tags only when exporting training data.
- Spelling variants that mean the same word are mapped to one canonical form using string-distance matching. — For Mova (tokens): A similar normalizer could merge British and American spellings and common typos before lemmatization, while keeping the original form for explanations.
- Because entities often arrive glued to function words, clitics are split off before tagging. — For Mova (tokens): The same principle applies to English possessive 's and contractions, which should be split consistently so that entity spans line up with UD tokens.
- Distant supervision is named as a way forward when hand-labelled data is scarce. — For Mova (learn): Entity labels projected from Wikidata could be added as an extra witness to the silver data, with the noise filtered by agreement with the other witnesses.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/)

## Cited by

Cited by

19 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 18 not yet.

- [TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT](https://md.research.elamur.ai/free/en/papers/melhem-2024-tao-stanceeval2024-shared-task-arabic/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{qu2023survey,
  title = {{A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends}},
  author = {Qu, Xiaoye and Gu, Yingjie and Xia, Qingrong and Li, Zechang and Wang, Zhefeng and Huai, Baoxing},
  year = {2023},
  doi = {10.48550/arxiv.2302.03512},
  eprint = {2302.03512},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2302.03512}
}
```

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## References

2 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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