---
title: "An Automatically Built Named Entity Lexicon for Arabic (2010)"
description: "Arabic had no large, well-organised lexicon of named entities. 95.83% precision. 27 references, cited by 11 papers here."
authors: ["Mohammed Attia", "Antonio Toral", "Lamia Tounsi", "Monica Monachini", "Josef van Genabith"]
year: 2010
venue: "Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)"
source: "https://aclanthology.org/L10-1548/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/attia-2010-automatically-built-named-entity-lexicon/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/attia-2010-automatically-built-named-entity-lexicon/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2010](https://md.research.elamur.ai/free/en/papers/2010/page/2/) / An Automatically Built Named Entity Lexicon for Arabic

# An Automatically Built Named Entity Lexicon for Arabic

**Authors:** [Mohammed Attia](https://md.research.elamur.ai/free/en/authors/mohammed-attia/), [Antonio Toral](https://md.research.elamur.ai/free/en/authors/antonio-toral/), [Lamia Tounsi](https://md.research.elamur.ai/free/en/authors/lamia-tounsi/), [Monica Monachini](https://md.research.elamur.ai/free/en/authors/monica-monachini/) and [Josef van Genabith](https://md.research.elamur.ai/free/en/authors/josef-van-genabith/) · **Year:** 2010 · **Venue:** [Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2010](https://md.research.elamur.ai/free/en/venues/lrec/2010/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L10-1548/) · [PDF](https://aclanthology.org/L10-1548.pdf) · [OpenAlex](https://openalex.org/W1481490983)

**Citations:** 34 citations (OpenAlex, 2026-10-14) · [11](https://md.research.elamur.ai/free/en/papers/attia-2010-automatically-built-named-entity-lexicon/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic had no large, well-organised lexicon of named entities. The usual cue for spotting proper names, capital letters, does not exist in Arabic script, and attached clitics such as conjunctions and prepositions further hide names in text. The authors port the MINELex method, previously used for English, Spanish and Italian, to Arabic.

They first collect the instantiable nouns of Arabic WordNet (AWN), adding nouns reached through its links to Princeton WordNet and all their hyponyms. They lemmatise Arabic Wikipedia (AWK) categories with MADA+TOKAN, string-match them to those nouns, and gather articles from the matched categories and their sub-categories. An article counts as a NE if its interlanguage counterparts in ten Latin-script languages are capitalised. Articles without such links fall back to keyword search in the abstract and a geonames lookup. Extra NEs outside AWN's reach are added afterwards, and vowel marks are restored.

The result is about 45,000 entries stored in the LMF ISO format. Against a manual gold standard, precision is 95.83–99.31% depending on a threshold.

## Key points
- Resources: Arabic WordNet (11,269 synsets, 23,481 expressions, 1,142 NEs) and Arabic Wikipedia (about 104,000 articles in September 2009). Penn Arabic Treebank statistics show NEs make up roughly 10% of all words.
- Mapping started from 1,572 instantiable synsets (384 from AWN, 1,475 from PWN), covering 1,187 nouns. Only 40.6% of these nouns could be matched to AWK categories.
- To decide NE status, the method checks capitalisation in ten languages: English, Spanish, French, Italian, Portuguese, Catalan and Romanian, together with Dutch, Swedish and Norwegian. However, only 62.5% of AWK articles have such interwiki links.
- Diacritisation combines geonames matching, MADA-TOKAN and heuristics. The lexicon stores both pointed and unpointed forms and links entries to SUMO (e.g. PoliticalOrganization).
- Evaluation shows a threshold trade-off: 95.83% precision at 66.13% recall, or 99.31% precision at 61.45% recall. Each entry carries a confidence value, e.g. a "wiki10" mode with 0.996.

## Interesting ideas
- When a language lacks a cue for proper names, the paper takes that cue from aligned entries in other languages that do have it.
- Instantiability is never stated directly, so the paper infers it: any synset that has instances is instantiable, and the property passes down to its hyponyms.
- A single prefix pattern ("category name + space + anything") was enough to recognise hyponym subcategories, which shows that cheap structural rules can recover a taxonomy.
- Each entry gets a confidence score from how many languages agree, and a tunable threshold then sets the balance between precision and recall.
- Validation works as a cascade: the strongest signal goes first, and weaker heuristics (abstract keywords, gazetteer lookup) cover only the items it cannot reach.

## For Mova

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

- **I1** (tokens): Sentence-initial or all-caps English words could be labelled PROPN vs NOUN by checking how the matching Wikidata item is capitalised in other languages.
- **I2** (world): This rule could mark which WordNet nouns can head named entities in the ontology. That would help the world model decide whether a mention is a specific individual or a kind.
- **I3** (world): Similar transparent patterns over Wikipedia or Wikidata category names could add is-a links to the knowledge graph without opaque learned models.
- **I4** (learn): Silver parses could likewise be scored by how many witness parsers agree. The admission threshold could be set from measured precision/recall rather than fixed by hand.
- **I5** (learn): Repair rules and teacher tiers could be ordered the same way, so cheap reliable evidence decides most cases and costlier fallbacks handle only the leftovers.

*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

- [Dublin City University](https://md.research.elamur.ai/free/en/universities/dublin-city-university/) — University, Dublin, Ireland · 70 works · 9,447 citations
- [National Research Council](https://md.research.elamur.ai/free/en/ngos/national-research-council/) — Nonprofit, Rome, Italy · 15 works · 15,649 citations
- [Institute for Computational Linguistics “A. Zampolli”](https://md.research.elamur.ai/free/en/labs/institute-for-computational-linguistics-a-zampolli/) — Research institute, Pisa, Italy · 14 works · 1,028 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🏷️ Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · 200 works

## Cite

Cite as: Mohammed Attia, Antonio Toral, Lamia Tounsi, Monica Monachini and Josef van Genabith. 2010. An Automatically Built Named Entity Lexicon for Arabic. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10).

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

```bibtex
@inproceedings{attia2010automatically,
  title = {{An Automatically Built Named Entity Lexicon for Arabic}},
  author = {Mohammed Attia, Antonio Toral, Lamia Tounsi, Monica Monachini and Josef {van Genabith}},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  doi = {10.63317/2v6fggyoqhpq},
  url = {https://doi.org/10.63317/2v6fggyoqhpq}
}
```

- [← previous Mining the Web for the Induction of a Dialectical Arabic Lexicon](https://md.research.elamur.ai/free/en/papers/sabbagh-2010-mining-web-induction-dialectical-arabic/)
- [next → Automatically Identifying the Source Words of Lexical Blends in English](https://md.research.elamur.ai/free/en/papers/cook-2010-automatically-identifying-source-words-lexical/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/page/2/)

## References

27 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Saleem Abuleil. 2004. Extracting names from arabic text for question-answering systems. In RIAO’04, pages 638–647, University of Avignon (Vaucluse), France. [find](https://scholar.google.com/scholar?q=Saleem+Abuleil%2E+2004%2E+Extracting+names+from+arabic+text+for+question%2Danswering+systems%2E+In+RIAO%E2%80%9904%2C+pages+638%E2%80%93647%2C+University+of+Avignon+%28Vaucluse%29%2C+France%2E)
2. Musa Alkhalifa and Horacio Rodrı́guez. 2009. Automatically extending ne coverage of arabic wordnet using wikipedia. In CITALA’09, Rabat, Morocco. [find](https://scholar.google.com/scholar?q=Musa+Alkhalifa+and+Horacio+Rodr%C4%B1%CC%81guez%2E+2009%2E+Automatically+extending+ne+coverage+of+arabic+wordnet+using+wikipedia%2E+In+CITALA%E2%80%9909%2C+Rabat%2C+Morocco%2E)
3. Y. Benajiba and P. Rosso. 2007. Anersys 2.0 : Conquering the ner task for the arabic language by combining the maximum entropy with pos-tag information. In IICAI-2007, Pune, India. [find](https://scholar.google.com/scholar?q=Y%2E+Benajiba+and+P%2E+Rosso%2E+2007%2E+Anersys+2%2E0+%3A+Conquering+the+ner+task+for+the+arabic+language+by+combining+the+maximum+entropy+with+pos%2Dtag+information%2E+In+IICAI%2D2007%2C+Pune%2C+India%2E)
4. Y. Benajiba, M. Diab, and P. Rosso. 2008. [Arabic named entity recognition using optimized feature sets](https://md.research.elamur.ai/free/en/papers/benajiba-2008-arabic-named-entity-recognition-optimized/). In EMNLP-2008, Honolulu, Hawaii.
5. Jordi Daudé, Lluı́s Padró, and German Rigau. 2003. Making wordnet mappings robust. In Proceedings of the 19th Congreso de la Sociedad Española para el Procesamiento del Lenguage Natural, SEPLN, Universidad Universidad de Alcalá de Henares. Madrid, Spain. [find](https://scholar.google.com/scholar?q=Jordi+Daude%CC%81%2C+Llu%C4%B1%CC%81s+Padro%CC%81%2C+and+German+Rigau%2E+2003%2E+Making+wordnet+mappings+robust%2E+In+Proceedings+of+the+19th+Congreso+de+la+Sociedad+Espan%CC%83ola+para+el+Procesamiento+del+Lenguage+Natural%2C+SEPLN%2C+Uni)
6. S. Elkateb, W. Black, H. Rodrı́guez, M. Alkhalifa, P. Vossen, A. Pease, and C. Fellbaum. 2006. [Building a wordnet for arabic](https://md.research.elamur.ai/free/en/papers/elkateb-2006-building-wordnet-arabic/). In LREC’06, Genoa, Italy.
7. A. Elsebai, F. Meziane, and F.Z. Belkredim. 2009. A rule based persons names arabic extraction system. In The IBIMA, Cairo, Egypt. [find](https://scholar.google.com/scholar?q=A%2E+Elsebai%2C+F%2E+Meziane%2C+and+F%2EZ%2E+Belkredim%2E+2009%2E+A+rule+based+persons+names+arabic+extraction+system%2E+In+The+IBIMA%2C+Cairo%2C+Egypt%2E)
8. Husni Al-Muhtaseb Mustafa Elshafei and Mansour Al-Ghamdi. 2006. Statistical methods for automatic diacritization of arabic text. In The Saudi 18th National Computer Conference (NCC18), Riyadh, Saudi Arabia. [find](https://scholar.google.com/scholar?q=Husni+Al%2DMuhtaseb+Mustafa+Elshafei+and+Mansour+Al%2DGhamdi%2E+2006%2E+Statistical+methods+for+automatic+diacritization+of+arabic+text%2E+In+The+Saudi+18th+National+Computer+Conference+%28NCC18%29%2C+Riyadh%2C+Saudi+A)
9. Benjamin Farber, Dayne Freitag, Nizar Habash, and Owen Rambow. 2008. [Improving ner in arabic using a morphological tagger](https://md.research.elamur.ai/free/en/papers/farber-2008-improving-ner-arabic-morphological-tagger/). In LREC’08, Marrakech, Morocc.
10. C. Fellbaum. 1998. WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, May. [find](https://scholar.google.com/scholar?q=C%2E+Fellbaum%2E+1998%2E+WordNet%3A+An+Electronic+Lexical+Database+%28Language%2C+Speech%2C+and+Communication%29%2E+The+MIT+Press%2C+May%2E)
11. Sergio Ferrández, Antonio Toral, Óscar Ferrández, Antonio Ferrández, and Rafael Muñoz. 2007. Applying wikipedia’s multilingual knowledge to cross-lingual question answering. In Zoubida Kedad, Nadira Lam-mari, Elisabeth Métais, Farid Meziane, and Yacine Rezgui, editors, NLDB, volume 4592 of Lecture Notes in Computer Science. Springer. [find](https://scholar.google.com/scholar?q=Sergio+Ferra%CC%81ndez%2C+Antonio+Toral%2C+O%CC%81scar+Ferra%CC%81ndez%2C+Antonio+Ferra%CC%81ndez%2C+and+Rafael+Mun%CC%83oz%2E+2007%2E+Applying+wikipedia%E2%80%99s+multilingual+knowledge+to+cross%2Dlingual+question+answering%2E+In+Zoubida+Kedad%2C+Nad)
12. G. Francopoulo, N. Bel, M. George, N. Calzolari, M. Monachini, M. Pet, and C. Soria. 2009. Multilingual resources for nlp in the lexical markup framework. In Language Resources and Evaluation Journal (forthcoming). [find](https://scholar.google.com/scholar?q=G%2E+Francopoulo%2C+N%2E+Bel%2C+M%2E+George%2C+N%2E+Calzolari%2C+M%2E+Monachini%2C+M%2E+Pet%2C+and+C%2E+Soria%2E+2009%2E+Multilingual+resources+for+nlp+in+the+lexical+markup+framework%2E+In+Language+Resources+and+Evaluation+Journal+)
13. F. Gey. 2000. Research to improve cross-language retrieval - position paper for clef. In Lecture Notes in Computer Science 2069, pages 83–88, Berlin: Springer. [find](https://scholar.google.com/scholar?q=F%2E+Gey%2E+2000%2E+Research+to+improve+cross%2Dlanguage+retrieval+%2D+position+paper+for+clef%2E+In+Lecture+Notes+in+Computer+Science+2069%2C+pages+83%E2%80%9388%2C+Berlin%3A+Springer%2E)
14. Nizar Habash and Owen Rambow. 2005. [Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop](https://md.research.elamur.ai/free/en/papers/habash-2005-arabic-tokenization-part-speech-tagging/). In Proceedings of ACL05, pages 573–580, Ann Arbor, Michigan. ACL.
15. Nizar Habash, Owen Rambow, and Ryan Roth. 2009. Mada+tokan: A toolkit for arabic tokenization, diacritization, morphological disambiguation, pos tagging, stemming and lemmatization. In The 2nd International Conference on Arabic Language Resources and Tools (MEDAR), Cairo, Egypt. [find](https://scholar.google.com/scholar?q=Nizar+Habash%2C+Owen+Rambow%2C+and+Ryan+Roth%2E+2009%2E+Mada%2Btokan%3A+A+toolkit+for+arabic+tokenization%2C+diacritization%2C+morphological+disambiguation%2C+pos+tagging%2C+stemming+and+lemmatization%2E+In+The+2nd+Interna)
16. A. Khemakhem, B. Gargouri, A. Abdelwahed, and G. Francopoulo. 2007. Modélisation des paradigmes de flexion des verbes arabes selon la norme lmf - iso 24613. In Proceedings of TALN. [find](https://scholar.google.com/scholar?q=A%2E+Khemakhem%2C+B%2E+Gargouri%2C+A%2E+Abdelwahed%2C+and+G%2E+Francopoulo%2E+2007%2E+Mode%CC%81lisation+des+paradigmes+de+flexion+des+verbes+arabes+selon+la+norme+lmf+%2D+iso+24613%2E+In+Proceedings+of+TALN%2E)
17. N. Loukil, K. Haddar, and A. Ben Hamadou. 2007. Normalisation de la représentation des lexiques syntaxiques arabes pour les formalismes d’unification. In Proceedings of Colloque Lexique et Grammaire. [find](https://scholar.google.com/scholar?q=N%2E+Loukil%2C+K%2E+Haddar%2C+and+A%2E+Ben+Hamadou%2E+2007%2E+Normalisation+de+la+repre%CC%81sentation+des+lexiques+syntaxiques+arabes+pour+les+formalismes+d%E2%80%99unification%2E+In+Proceedings+of+Colloque+Lexique+et+Grammaire%2E)
18. J. Maloney and M. Niv. 1998. Tagarab: A fast, accurate arabic name recogniser using high precision morphological analysis. In The Workshop on Computational Approaches to Semitic Languages, pages 8–15, Montreal, Canada. [find](https://scholar.google.com/scholar?q=J%2E+Maloney+and+M%2E+Niv%2E+1998%2E+Tagarab%3A+A+fast%2C+accurate+arabic+name+recogniser+using+high+precision+morphological+analysis%2E+In+The+Workshop+on+Computational+Approaches+to+Semitic+Languages%2C+pages+8%E2%80%9315%2C)
19. S. Mesfar. 2007. Named entity recognition for arabic using syntactic grammars. In The 12th International Conference on Application of Natural Language to Information Systems, pages 305–316, Paris, France. [find](https://scholar.google.com/scholar?q=S%2E+Mesfar%2E+2007%2E+Named+entity+recognition+for+arabic+using+syntactic+grammars%2E+In+The+12th+International+Conference+on+Application+of+Natural+Language+to+Information+Systems%2C+pages+305%E2%80%93316%2C+Paris%2C+Fra)
20. Rani Nelken and Stuart M. Shieber. 2005. [Arabic diacritization using weighted finite-state transducers](https://md.research.elamur.ai/free/en/papers/nelken-2005-arabic-diacritization-weighted-finite-state/). In The ACL 2005 Workshop On Computational Approaches To Semitic Languages the ACL 2005 Workshop On Computational Approaches To Semitic Languages, Ann Arbor, Michigan.
21. M. Rashwan, M. Al-Badrashiny, M. Attia, and S. Abdou. 2009. A hybrid system for automatic arabic diacritization. In The 2nd International Conference on Arabic Language Resources and Tools, Cairo, Egypt. [find](https://scholar.google.com/scholar?q=M%2E+Rashwan%2C+M%2E+Al%2DBadrashiny%2C+M%2E+Attia%2C+and+S%2E+Abdou%2E+2009%2E+A+hybrid+system+for+automatic+arabic+diacritization%2E+In+The+2nd+International+Conference+on+Arabic+Language+Resources+and+Tools%2C+Cairo%2C+Egyp)
22. R. Rodrı́guez, D. Farwell, J. Farreres, M. Bertran, M. Alkhalifa, M.A. Mart., W. Black, S. Elkateb, J. Kirk, A. Pease, P. Vossen, and C. Fellbaum. 2008. Arabic wordnet: Current state and future extensions. In The Fourth Global WordNet Conference, Szeged, Hungary. [find](https://scholar.google.com/scholar?q=R%2E+Rodr%C4%B1%CC%81guez%2C+D%2E+Farwell%2C+J%2E+Farreres%2C+M%2E+Bertran%2C+M%2E+Alkhalifa%2C+M%2EA%2E+Mart%2E%2C+W%2E+Black%2C+S%2E+Elkateb%2C+J%2E+Kirk%2C+A%2E+Pease%2C+P%2E+Vossen%2C+and+C%2E+Fellbaum%2E+2008%2E+Arabic+wordnet%3A+Current+state+and+future+extens)
23. Doaa Samy, Antonio Moreno, and Jose M. Guirao. 2005. A proposal for an arabic named entity tagger leveraging a parallel corpus. In RANLP, Borovets, Bulgaria. [find](https://scholar.google.com/scholar?q=Doaa+Samy%2C+Antonio+Moreno%2C+and+Jose+M%2E+Guirao%2E+2005%2E+A+proposal+for+an+arabic+named+entity+tagger+leveraging+a+parallel+corpus%2E+In+RANLP%2C+Borovets%2C+Bulgaria%2E)
24. K. Shaalan and H. Raza. 2009. Nera: Named entity recognition for arabic. In JASIST, John Wiley and Sons, pages 1652–1663, NJ, USA. [↗](https://doi.org/10.1002/asi.21090)
25. Antonio Toral. 2009. Enrichment of Language Resources by Exploiting New Text and the Resources Themselves. A case study on the acquisition of a NE lexicon. Ph.D. thesis, Universitat d’Alacant. [find](https://scholar.google.com/scholar?q=Antonio+Toral%2E+2009%2E+Enrichment+of+Language+Resources+by+Exploiting+New+Text+and+the+Resources+Themselves%2E+A+case+study+on+the+acquisition+of+a+NE+lexicon%2E+Ph%2ED%2E+thesis%2C+Universitat+d%E2%80%99Alacant%2E)
26. P. Vossen. 1998. EuroWordNet A Multilingual Database with Lexical Semantic Networks. Kluwer Academic publishers. [↗](https://doi.org/10.1007/978-94-017-1491-4)
27. Imed Zitouni and Ruhi Sarikaya. 2009. Arabic diacritic restoration approach based on maximum entropy models. In Computer Speech Language, volume Volume 23, Issue 3, pages 257–276. [↗](https://doi.org/10.1016/j.csl.2008.06.001)

*Updated 1 November 2026*