---
title: "A Survey of Arabic Named Entity Recognition and Classification"
description: "This survey maps the state of Arabic Named Entity Recognition (NER), the task of finding and… 110 references, cited by 29 papers here."
authors: ["Khaled Shaalan"]
year: 2014
venue: "Computational Linguistics"
source: "https://aclanthology.org/J14-2008/"
doi: "10.1162/coli_a_00178"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shaalan-2014-survey-arabic-named-entity-recognition/"
---
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# A Survey of Arabic Named Entity Recognition and Classification

**Authors:** [Khaled Shaalan](https://md.research.elamur.ai/free/en/authors/khaled-shaalan/) · **Year:** 2014 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2014](https://md.research.elamur.ai/free/en/venues/cl/2014/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/J14-2008/) · [PDF](https://aclanthology.org/J14-2008.pdf) · [DOI 10.1162/coli\_a\_00178](https://doi.org/10.1162/coli_a_00178) · [OpenAlex](https://openalex.org/W2033599040)

**Citations:** 213 citations (OpenAlex, 2026-10-14) · [29](https://md.research.elamur.ai/free/en/papers/shaalan-2014-survey-arabic-named-entity-recognition/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey maps the state of Arabic Named Entity Recognition (NER), the task of finding and classifying names, temporal expressions and numeric expressions in text. Arabic is hard for NER for several reasons. Its morphology is rich, so one word can hold several mentions. It has no capitalization, words are highly ambiguous, and written text follows few strict standards. Shaalan covers:
- why NER matters for retrieval, QA, translation, clustering and navigation;
- the linguistic challenges and annotation standards;
- the available corpora and gazetteers;
- the three families of systems (rule-based, ML-based, hybrid);
- feature choices, tooling, Precision/Recall/F-measure evaluation, and a review of published systems.

The main conclusions are:
- Arabic resources remain scarce and often expensive.
- Rule-based systems persist because of that scarcity and their strong results.
- Hybrids that feed rule decisions into an ML classifier bring significant gains.
- Using the full combination of features usually performs best.

The survey matters as an entry point to the literature. It also offers lessons for other morphologically rich languages.

## Key points
- NER began as an information extraction task at the Message Understanding Conferences in the 1990s. The classic classes are person, location and organization, which can be refined into subtype hierarchies.
- Guo et al. (2009) found that about 71% of search-engine queries contain named entities.
- Arabic pre-processing commonly relies on three tools. BAMA and its successor MADA handle morphology, and AMIRA handles base phrase chunking (BPC). GATE is highlighted for rule-based development because of its built-in gazetteers and rules.
- ANERsys provides free corpora and gazetteers, which allow systems to be compared under different settings. Bootstrapping is used to grow resources from Web and multilingual corpora.
- Arabic NER work concentrates on person, location and organization names in newswire text.

## Interesting ideas
- Hybrid systems take the output of hand-written rules and pass it to a statistical classifier as extra input features, so the learner can trust or override the rules.
- The survey insists on choosing the recognition unit explicitly: a real-world entity, a single mention, a co-reference chain, or a relation. Each step up is considerably harder.
- Question words predict the answer's entity type: "who" points to persons or organizations, "where" to locations, and "when" to temporal expressions.
- Translation should depend on entity type. Person names get transliterated, while organization names get mostly translated. For locations, the name part is transliterated and the category word (e.g., "mountains") is translated.
- Leading and trailing character n-grams act as cheap stand-ins for full morphological analysis. Borrowing capitalization cues from aligned English text compensates for a script with no capitals.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Self-induced repair rules could become features for the trained parser or tagger, instead of acting as hard post-edits, which keeps their effect measurable.
- **I2** (world): The world of a text should keep mentions and entities as separate layers, with explicit links between them, so each grouping decision stays inspectable.
- **I3** (answer): The readers could use the question word to narrow which world entities may answer before searching, and could explain a rejected candidate by its type mismatch.
- **I4** (back): Generation and translation through the meta-world could attach a rendering policy to each entity type, so names surface correctly instead of being translated word by word.
- **I5** (tokens): Affix n-gram features could back up the tagger and lemmatizer on unknown or noisy words where the full morphology analysis fails.

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

## Institutions

- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 381 works · 80,747 citations
- [British University in Dubai](https://md.research.elamur.ai/free/en/universities/british-university-in-dubai/) — University, Dubai, United Arab Emirates · 11 works · 589 citations

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## Cite

Cite as: Khaled Shaalan. 2014. A Survey of Arabic Named Entity Recognition and Classification. Computational Linguistics.

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

```bibtex
@article{shaalan2014survey,
  title = {{A Survey of Arabic Named Entity Recognition and Classification}},
  author = {Khaled Shaalan},
  journal = {Computational Linguistics},
  year = {2014},
  doi = {10.1162/coli\_a\_00178},
  url = {https://doi.org/10.1162/coli_a_00178}
}
```

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## References

110 references, 24 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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*Updated 1 November 2026*