---
title: "Hybrid approach for transliteration of Algerian arabizi: a primary"
description: "The authors propose a hybrid rule-plus-statistics transliterator from Algerian Arabizi to Arabic script that… 75.11% precision. 18 references."
authors: ["Imane Guellil", "Faical Azouaou", "Fodil Benali", "Ala-Eddine Hachani", "Houda Saadane"]
year: 2018
venue: "arXiv:1808.03437 · 25e conf\\'erence sur le Traitement Automatique des Langues Naturelles (TALN), May 2018, Rennes, France"
source: "https://arxiv.org/abs/1808.03437"
doi: "10.48550/arxiv.1808.03437"
arxiv: "1808.03437"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/guellil-2018-hybrid-approach-transliteration-algerian-arabizi/"
---
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# Hybrid approach for transliteration of Algerian arabizi: a primary study

**Authors:** [Imane Guellil](https://md.research.elamur.ai/free/en/authors/imane-guellil/), [Faical Azouaou](https://md.research.elamur.ai/free/en/authors/faical-azouaou/), Fodil Benali, Ala-Eddine Hachani and [Houda Saadane](https://md.research.elamur.ai/free/en/authors/houda-saadane/) · **Year:** 2018 · **Venue:** arXiv:1808.03437 · 25e conf\\'erence sur le Traitement Automatique des Langues Naturelles (TALN), May 2018, Rennes, France · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1808.03437) · [PDF](https://arxiv.org/pdf/1808.03437) · [DOI 10.48550/arxiv.1808.03437](https://doi.org/10.48550/arxiv.1808.03437) · [arXiv 1808.03437](https://arxiv.org/abs/1808.03437) · [OpenAlex](https://openalex.org/W2887607033)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Algerian dialect speakers often write online in Arabizi, a spontaneous romanized script that mixes Latin letters, digits and symbols. Most dialect-processing tools expect Arabic script, so this text cannot be processed directly. The authors propose a hybrid rule-plus-statistics transliterator from Algerian Arabizi to Arabic script that does not need a parallel Arabizi–Arabic corpus.

The method has four stages:
- **Normalization.** Letter elongations are collapsed, for example "hiaaaaati" becomes "hiati".
- **Letter mapping.** A table maps each Latin letter or digit to its possible Arabic letters, and position-dependent rules refine it. Vowels may also map to nothing.
- **Candidate generation.** The mappings and rules produce every possible Arabic spelling of the word.
- **Ranking.** Candidates are scored against a monolingual Arabic-script corpus of Algerian comments that the authors collected from Facebook. Scoring uses either raw frequency lookup or a language model.

Evaluated on three test corpora, the best setting reached 75.11% precision. The authors call this competitive with general Arabizi transliteration work. The approach matters because it replaces costly parallel data with easy-to-collect monolingual text.

## Key points
- **Four problems.** Arabizi transliteration has to deal with:
  - vowel handling, where a vowel can map to several Arabic letters or to none;
  - one Latin letter covering several Arabic letters (for example 't' → ت or ط);
  - context-dependent ambiguity (for example 'matar' as "rain" vs "airport");
  - code-switching with French and English.
- **Data.**
  - The Arabic-script corpus comes from 133 Algerian Facebook pages, such as Ooredoo, Djezzy and Hamoud Boualem.
  - It is complemented by a dialect dictionary built partly from the PADIC corpus.
- **Candidate generation.**
  - The letter table follows a Wikipedia-derived Arabic chat alphabet mapping, similar to van der Wees et al. (2016), extended with Algerian-specific rules.
  - Example sizes: "kraht" yields 32 candidates and "hiati" yields 16.
  - Unlike earlier systems, vowels can be dropped, so the correct form of words like '3afsa' becomes reachable.
- **Result.** Best precision was 75.11% across three test corpora.
- **Remaining errors** come from two sources:
  - ignoring sentence context, since each word is treated as a unigram;
  - French-origin words that are missing from the Arabic corpus (for example 'lafichage').

## Interesting ideas
- Generate an over-complete candidate set with simple rules, then let a corpus pick the winner; this cleanly separates "what is possible" from "what is likely"
- Explicitly allow a "null" mapping (deleting a character) during generation, because the right answer often lies outside naive one-to-one substitution
- Replace expensive parallel data with a large monolingual target-side corpus used as the arbiter of candidate plausibility
- The error analysis shows that per-word decisions fail exactly where context decides the reading, and the proposed fix is to rank n-gram candidates instead of single words
- Use the hybrid system to bootstrap a semi-supervised parallel corpus that can later train a purely statistical model

## For Mova

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

- **I1** (tokens): Noisy or non-standard spellings could be normalized by expanding rule-based variants and ranking them against frequency counts from clean English text.
- **I2** (tokens): Spelling or abbreviation repair in the tokenizer could include deletion and insertion options, so that dropped-vowel forms like "msg" or "pls" can be resolved to lemmas.
- **I3** (learn): Silver data could be filtered by checking candidate analyses against monolingual statistics instead of hand-annotated pairs, which cuts teacher cost.
- **I4** (tree): When the parser picks among ambiguous lexical readings, scoring them jointly with neighbouring words rather than in isolation should cut errors of the "rain vs airport" kind.
- **I5** (learn): Rule-generated, corpus-ranked outputs could become silver training data for a learned component, with a negative-control check on held-out gold data.

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

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## Cite

Cite as: Imane Guellil, Faical Azouaou, Fodil Benali, Ala-Eddine Hachani and Houda Saadane. 2018. Hybrid approach for transliteration of Algerian arabizi: a primary study. arXiv:1808.03437 · 25e conf\\'erence sur le Traitement Automatique des Langues Naturelles (TALN), May 2018, Rennes, France.

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

```bibtex
@misc{guellil2018hybrid,
  title = {{Hybrid approach for transliteration of Algerian arabizi: a primary study}},
  author = {Imane Guellil, Faical Azouaou, Fodil Benali, Ala-Eddine Hachani and Houda Saadane},
  year = {2018},
  doi = {10.48550/arxiv.1808.03437},
  eprint = {1808.03437},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1808.03437}
}
```

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## References

18 references, 1 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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2. Cotterell R., Renduchintala A., Saphra N., &Callison-Burch, C. (2014).An algerianarabic-french code-switched corpus. Paper presented at the Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools Workshop Programme. [find](https://scholar.google.com/scholar?q=Cotterell+R%2E%2C+Renduchintala+A%2E%2C+Saphra+N%2E%2C+%26Callison%2DBurch%2C+C%2E+%282014%29%2EAn+algerianarabic%2Dfrench+code%2Dswitched+corpus%2E+Paper+presented+at+the+Workshop+on+Free%2FOpen%2DSource+Arabic+Corpora+and+Corpora+Proc)
3. Darwish K. (2013). Arabizi detection and conversion to Arabic.arXiv preprint arXiv:1306.6755. [↗](https://arxiv.org/abs/1306.6755)
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7. Guellil I., AZOUAOU F. (2017b). Neural Vs Statistical Translation of Algerian Arabic Dialect written with Arabizi and Arabic letter. The 31st Pacific Asia Conference on Language, Information and Computation PACLIC. [find](https://scholar.google.com/scholar?q=Guellil+I%2E%2C+AZOUAOU+F%2E+%282017b%29%2E+Neural+Vs+Statistical+Translation+of+Algerian+Arabic+Dialect+written+with+Arabizi+and+Arabic+letter%2E+The+31st+Pacific+Asia+Conference+on+Language%2C+Information+and+Compu)
8. Guellil., I et AZOUAOU F. (2017). Bilingual Lexicon for Algerian Arabic Dialect Treatment in Social Media. WiNLP: Women & Underrepresented Minorities in Natural Language Processing (co-located with ACL 2017) [find](https://scholar.google.com/scholar?q=Guellil%2E%2C+I+et+AZOUAOU+F%2E+%282017%29%2E+Bilingual+Lexicon+for+Algerian+Arabic+Dialect+Treatment+in+Social+Media%2E+WiNLP%3A+Women+%26+Underrepresented+Minorities+in+Natural+Language+Processing+%28co%2Dlocated+with+AC)
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10. Josan G. S., &Lehal, G. S. (2010).A Punjabi to Hindi Machine Transliteration System.Computational Linguistics and Chinese Language Processing, 15(2), 77-102. [find](https://scholar.google.com/scholar?q=Josan+G%2E+S%2E%2C+%26Lehal%2C+G%2E+S%2E+%282010%29%2EA+Punjabi+to+Hindi+Machine+Transliteration+System%2EComputational+Linguistics+and+Chinese+Language+Processing%2C+15%282%29%2C+77%2D102%2E)
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12. May J., Benjira Y., &Echihabi, A. (2014). An Arabizi-English social media statistical machine translation system. Paper presented at the Proceedings of the 11th Conference of the Association for Machine Translation in the Americas. [find](https://scholar.google.com/scholar?q=May+J%2E%2C+Benjira+Y%2E%2C+%26Echihabi%2C+A%2E+%282014%29%2E+An+Arabizi%2DEnglish+social+media+statistical+machine+translation+system%2E+Paper+presented+at+the+Proceedings+of+the+11th+Conference+of+the+Association+for+Machi)
13. Meftouh K., Harrat S., Jamoussi S., Abbas, M., &Smaili, K. (2015). [Machine translation experiments on PADIC: A parallel Arabic dialect corpus](https://md.research.elamur.ai/free/en/papers/meftouh-2015-machine-translation-experiments-padic-parallel/). Paper presented at the The 29th Pacific Asia conference on language, information and computation.
14. Saâdane H., &Habash, N. (2015).A conventional orthography for Algerian Arabic.In Proceedings of the Second Workshop on Arabic Natural Language Processing (pp. 69-79). [find](https://scholar.google.com/scholar?q=Sa%C3%A2dane+H%2E%2C+%26Habash%2C+N%2E+%282015%29%2EA+conventional+orthography+for+Algerian+Arabic%2EIn+Proceedings+of+the+Second+Workshop+on+Arabic+Natural+Language+Processing+%28pp%2E+69%2D79%29%2E)
15. Saâdane H., & Semmar N. (2012).Utilisation de la transliteration arabe pour l'amélioration de l'alignement de mots à partir de corpus parallèles français-arabe (UsingArabic Transliteration to Improve Word Alignment from French-ArabicParallelCorpora)[in French].Paper presented at the Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN. [find](https://scholar.google.com/scholar?q=Sa%C3%A2dane+H%2E%2C+%26+Semmar+N%2E+%282012%29%2EUtilisation+de+la+transliteration+arabe+pour+l%27am%C3%A9lioration+de+l%27alignement+de+mots+%C3%A0+partir+de+corpus+parall%C3%A8les+fran%C3%A7ais%2Darabe+%28UsingArabic+Transliteration+to+Improve+)
16. Saâdane H., Nouvel,. D., Seffih H., &Fluhr, C. (2017). Une approche linguistique pour la détection des dialectes arabes. Paper presented at the 24e Conférence sur le Traitement Automatique des Langues Naturelles (TALN). [find](https://scholar.google.com/scholar?q=Sa%C3%A2dane+H%2E%2C+Nouvel%2C%2E+D%2E%2C+Seffih+H%2E%2C+%26Fluhr%2C+C%2E+%282017%29%2E+Une+approche+linguistique+pour+la+d%C3%A9tection+des+dialectes+arabes%2E+Paper+presented+at+the+24e+Conf%C3%A9rence+sur+le+Traitement+Automatique+des+Langues)
17. Rosca M., &Breuel T. (2016). Sequence-to-sequence neural network models for transliteration. arXiv preprint arXiv:1610.09565. [↗](https://arxiv.org/abs/1610.09565)
18. Saâdane H., Guidere M., &Fluhr C. (2013).La reconnaissance automatique des dialectes arabes à l’écrit. Paper presented at the Colloque International Traduction et Champs Connexes, Quelle Place Pour La Langue Arabe Aujourd’hui. van der Wees, M., Bisazza A., &Monz C. (2016). A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation. WNUT 2016, 43. [find](https://scholar.google.com/scholar?q=Sa%C3%A2dane+H%2E%2C+Guidere+M%2E%2C+%26Fluhr+C%2E+%282013%29%2ELa+reconnaissance+automatique+des+dialectes+arabes+%C3%A0+l%E2%80%99%C3%A9crit%2E+Paper+presented+at+the+Colloque+International+Traduction+et+Champs+Connexes%2C+Quelle+Place+Pour+La)

*Updated 24 October 2026*