---
title: "Using Ambiguity Detection to Streamline Linguistic Annotation"
description: "This creates orthographic ambiguity on top of ordinary lexical ambiguity, and it makes building… 38 references, cited by 1 paper here."
authors: ["Wajdi Zaghouani", "Abdelati Hawwari", "Sawsan Alqahtani", "Houda Bouamor", "Mahmoud Ghoneim", "Mona Diab", "Kemal Oflazer"]
year: 2016
venue: "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)"
source: "https://aclanthology.org/W16-4115/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zaghouani-2016-ambiguity-detection-streamline-linguistic-annotation/"
---
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# Using Ambiguity Detection to Streamline Linguistic Annotation

**Authors:** [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/), [Abdelati Hawwari](https://md.research.elamur.ai/free/en/authors/abdelati-hawwari/), [Sawsan Alqahtani](https://md.research.elamur.ai/free/en/authors/sawsan-alqahtani/), [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Mahmoud Ghoneim](https://md.research.elamur.ai/free/en/authors/mahmoud-ghoneim/), [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/) and [Kemal Oflazer](https://md.research.elamur.ai/free/en/authors/kemal-oflazer/) · **Year:** 2016 · **Venue:** [Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2016](https://md.research.elamur.ai/free/en/venues/coling/2016/)) · **Publisher:** [International Committee on Computational Linguistics (ICCL)](https://md.research.elamur.ai/free/en/publishers/international-committee-on-computational-linguistics-iccl/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/W16-4115/) · [PDF](https://aclanthology.org/W16-4115.pdf) · [OpenAlex](https://openalex.org/W2787700961)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most written Arabic omits short vowels and other diacritics. This creates orthographic ambiguity on top of ordinary lexical ambiguity, and it makes building diacritized training corpora slow and error-prone. Within the OptDiac project, the authors test whether automatically flagging ambiguous words can make manual diacritization faster and more consistent. Four native-speaker annotators and a lead annotator worked on texts from the Corpus of Contemporary Arabic (CCA) in MANDIAC, a web tool that records per-sentence annotation time. MADAMIRA pre-diacritized every word and offered its top three candidates; annotators picked one or edited the word by hand. A word was flagged as ambiguous when other analyses scored close to the best one, after variants differing only in unimportant marks were discarded. Comparing three experimental conditions, the authors report that ambiguity flags cut annotation time and raised inter-annotator agreement. A closing error analysis sorts the remaining disagreements into nine categories. The work shows how cheap confidence signals from an existing analyzer can focus human effort where it matters.

## Key points
- Data: the balanced CCA corpus, covering many genres from children's stories to science and recipes. It was normalized to XML, split into sentences and deduplicated. By contrast, only about 1.5% of tokens in the UN Arabic corpus carry any diacritic.
- Pre-annotation: MADAMIRA (96.0% lemmatization accuracy, 86.3% diacritization accuracy) supplies the top three diacritized candidates. Annotators may also edit words freely.
- Ambiguity rule: all analyses scoring within 15% of the top analysis are kept. Case and mood marks and third-person possessive pronoun diacritics are then stripped to remove redundant variants. Remaining competitors mark the word as ambiguous.
- Tooling: MANDIAC, built on the QAWI token editor from the QALB project, keeps undo/redo history and a per-sentence timer used to measure speed.
- Error analysis identifies nine disagreement types: lexical, morphological, POS, case endings, diptotes, phonology (sun/moon letters), regional pronunciation variants, level of diacritization, and typos.

## Interesting ideas
- An item counts as ambiguous when a rival analysis scores within a fixed margin of the winner, giving a simple, tunable trigger for human attention.
- Before deciding whether a word is ambiguous, merge alternatives that differ only in features nobody downstream cares about, so the flag fires only on meaningful differences.
- Give annotators a short ranked candidate list plus free editing, and time each item, so assistance quality can be measured as human cost saved.
- Sort disagreements into types to separate genuine linguistic ambiguity from guideline drift, regional variation and plain slips.
- Some ambiguity is visible to a single annotator, while some only appears when several annotations are compared.

## For Mova

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

- **I1** (learn): Mark silver-data tokens where witness parsers' best and second-best scores are close, and send only those to the LLM teacher tiers instead of re-checking everything.
- **I2** (tokens): Collapse tagger or morphology alternatives that differ only in features irrelevant to the UD tree, so ambiguity flags point to real choices.
- **I3** (learn): Log how often and how long a teacher or repair step overrides the top parser candidate, and use this as a cost-based metric for each proposer.
- **I4** (learn): Classify cases where witness parsers disagree, so true ambiguities are kept as alternatives while drift and typos feed the self-induced repair rules.
- **I5** (frames): When different parses yield different who-did-what-to-whom frames, keep both readings as explicit alternatives so later context can choose between them.

*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

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 539 works · 179,533 citations
- [George Washington University](https://md.research.elamur.ai/free/en/universities/george-washington-university/) — University, Washington, United States · 51 works · 8,148 citations

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## Cite

Cite as: Wajdi Zaghouani, Abdelati Hawwari, Sawsan Alqahtani, Houda Bouamor, Mahmoud Ghoneim, Mona Diab and Kemal Oflazer. 2016. Using Ambiguity Detection to Streamline Linguistic Annotation. Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC).

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

```bibtex
@inproceedings{zaghouani2016using,
  title = {{Using Ambiguity Detection to Streamline Linguistic Annotation}},
  author = {Wajdi Zaghouani, Abdelati Hawwari, Sawsan Alqahtani, Houda Bouamor, Mahmoud Ghoneim, Mona Diab and Kemal Oflazer},
  booktitle = {Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)},
  year = {2016},
  url = {https://aclanthology.org/W16-4115/}
}
```

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## References

38 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Gheith A Abandah, Alex Graves, Balkees Al-Shagoor, Alaa Arabiyat, Fuad Jamour, and Majid Al-Taee. 2015. Automatic diacritization of arabic text using recurrent neural networks. International Journal on Document Analysis and Recognition (IJDAR), 18(2):183–197. [↗](https://doi.org/10.1007/s10032-015-0242-2)
2. Latifa Al-Sulaiti and Eric Steven Atwell. 2006. The design of a corpus of contemporary arabic. International Journal of Corpus Linguistics, 11(2):135–171. [↗](https://doi.org/10.1075/ijcl.11.2.02als)
3. Eric Atwell, Nizar Habash, Bill Louw, Bayan Abu Shawar, Tony McEnery, Wajdi Zaghouani, and Mahmoud El-Haj. 2010. Understanding the quran: A new grand challenge for computer science and artificial intelligence. ACM-BCS Visions of Computer Science 2010. [find](https://scholar.google.com/scholar?q=Eric+Atwell%2C+Nizar+Habash%2C+Bill+Louw%2C+Bayan+Abu+Shawar%2C+Tony+McEnery%2C+Wajdi+Zaghouani%2C+and+Mahmoud+El%2DHaj%2E+2010%2E+Understanding+the+quran%3A+A+new+grand+challenge+for+computer+science+and+artificial+inte)
4. Petra Saskia Bayerl and Karsten Ingmar Paul. 2011. What determines inter-coder agreement in manual annotations? a meta-analytic investigation. Computational Linguistics, 37(4):699–725. [↗](https://doi.org/10.1162/coli_a_00074)
5. Yonatan Belinkov and James Glass. 2015. [Arabic diacritization with recurrent neural networks](https://md.research.elamur.ai/free/en/papers/belinkov-2015-arabic-diacritization-recurrent-neural-networks/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2281–2285, Lisbon, Portugal.
6. Houda Bouamor, Wajdi Zaghouani, Mona Diab, Ossama Obeid, Kemal Oflazer, Mahmoud Ghoneim, and Abdelati Hawwari. 2015. [A pilot study on arabic multi-genre corpus diacritization](https://md.research.elamur.ai/free/en/papers/bouamor-2015-pilot-study-arabic-multi-genre/). In Proceedings of the Association for Computational Linguistics Second Workshop on Arabic Natural Language Processing, pages 80–88, Beijing, China.
7. Tim Buckwalter. 2002. Buckwalter Arabic morphological analyzer version 1.0. Technical Report LDC2002L49, Linguistic Data Consortium. [↗](https://doi.org/10.35111/7vzm-mb15)
8. Mona Diab, Mahmoud Ghoneim, and Nizar Habash. 2007. [Arabic diacritization in the context of statistical machine translation](https://md.research.elamur.ai/free/en/papers/diab-2007-arabic-diacritization-context-statistical-machine/). In Proceedings of Machine Translation Summit (MT-Summit), Copenhagen, Denmark.
9. Mona Diab, Aous Mansouri, Martha Palmer, Olga Babko-Malaya, Wajdi Zaghouani, Ann Bies, and Mohammed Maamouri. 2008. A pilot arabic propbank. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC). [find](https://scholar.google.com/scholar?q=Mona+Diab%2C+Aous+Mansouri%2C+Martha+Palmer%2C+Olga+Babko%2DMalaya%2C+Wajdi+Zaghouani%2C+Ann+Bies%2C+and+Mohammed+Maamouri%2E+2008%2E+A+pilot+arabic+propbank%2E+In+Proceedings+of+the+7th+International+Conference+on+Langu)
10. Heba Elfardy and Mona Diab. 2012. [Aida: Automatic identification and glossing of dialectal arabic](https://md.research.elamur.ai/free/en/papers/elfardy-2012-aida-automatic-identification-glossing-dialectal/). In Proceedings of the 16th eamt conference (project papers), pages 83–83.
11. Michael Gamon, Jianfeng Gao, Chris Brockett, Alexandre Klementiev, William B. Dolan, Dmitriy Belenko, and Lucy Vanderwende. In Third International Joint Conference on Natural Language Processing, IJCNLP, Address=Hyderabad, India, Year=2008, Pages = 449–456, Title = [Using Contextual Speller Techniques and Language Modeling for ESL Error Correction](https://md.research.elamur.ai/free/en/papers/gamon-2008-contextual-speller-techniques-language-modeling/).
12. Ryu Iida, Mamoru Komachi, Kentaro Inui, and Yuji Matsumoto. 2007. [Annotating a japanese text corpus with predicate-argument and coreference relations](https://md.research.elamur.ai/free/en/papers/iida-2007-annotating-japanese-text-corpus-predicate/). In Proceedings of the Linguistic Annotation Workshop, LAW ’07, pages 132–139, Stroudsburg, PA, USA. Association for Computational Linguistics.
13. Emi Izumi, Kiyotaka Uchimoto, and Hitoshi Isahara. 2004. The NICT JLE corpus exploiting the language learners’ speech database for research and education. International Journal of The Computer, the Internet and Management, 12(2):119–125, May. [find](https://scholar.google.com/scholar?q=Emi+Izumi%2C+Kiyotaka+Uchimoto%2C+and+Hitoshi+Isahara%2E+2004%2E+The+NICT+JLE+corpus+exploiting+the+language+learners%E2%80%99+speech+database+for+research+and+education%2E+International+Journal+of+The+Computer%2C+the+In)
14. Youngim Jung and Hyuk-Chul Kwon. 2011. Consistency maintenance in prosodic labeling for reliable prediction of prosodic breaks. In Proceedings of the 5th Linguistic Annotation Workshop, LAW V ’11, pages 38–46, Stroudsburg, PA, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Youngim+Jung+and+Hyuk%2DChul+Kwon%2E+2011%2E+Consistency+maintenance+in+prosodic+labeling+for+reliable+prediction+of+prosodic+breaks%2E+In+Proceedings+of+the+5th+Linguistic+Annotation+Workshop%2C+LAW+V+%E2%80%9911%2C+pag)
15. Katrin Kirchhoff and Dimitra Vergyri. 2005. Cross-dialectal data sharing for acoustic modeling in arabic speech recognition. Speech Communication, 46(1):37–51. [↗](https://doi.org/10.1016/j.specom.2005.01.004)
16. Mohamed Maamouri, Ann Bies, Seth Kulick, Wajdi Zaghouani, David Graff, and Michael Ciul. 2010. From speech to trees: Applying treebank annotation to arabic broadcast news. In Proceedings of International Conference on Language Resources and Evaluation (LREC 2010). [find](https://scholar.google.com/scholar?q=Mohamed+Maamouri%2C+Ann+Bies%2C+Seth+Kulick%2C+Wajdi+Zaghouani%2C+David+Graff%2C+and+Michael+Ciul%2E+2010%2E+From+speech+to+trees%3A+Applying+treebank+annotation+to+arabic+broadcast+news%2E+In+Proceedings+of+Internatio)
17. Mohamed Maamouri, Wajdi Zaghouani, Violetta Cavalli-Sforza, Dave Graff, and Mike Ciul. 2012. [Developing aret: an nlp-based educational tool set for arabic reading enhancement](https://md.research.elamur.ai/free/en/papers/maamouri-2012-developing-aret-nlp-based-educational/). In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 127–135. Association for Computational Linguistics.
18. Behrang Mohit, Alla Rozovskaya, Nizar Habash, Wajdi Zaghouani, and Ossama Obeid. 2014. [The first qalb shared task on automatic text correction for arabic](https://md.research.elamur.ai/free/en/papers/mohit-2014-first-qalb-shared-task-automatic/). In Proceedings of the EMNLP Workshop on Arabic Natural Language Processing, page 39.
19. Ryo Nagata, Atsuo Kawai, Koichiro Morihiro, and Naoki Isu. 2006. [A feedback-augmented method for detecting errors in the writing of learners of english](https://md.research.elamur.ai/free/en/papers/nagata-2006-feedback-augmented-method-detecting-errors/). In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 241–248, Sydney, Australia.
20. Ossama Obeid, Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Kemal Oflazer, and Nadi Tomeh. 2013. A web-based annotation framework for large-scale text correction. In Sixth International Joint Conference on Natural Language Processing, page 1. [find](https://scholar.google.com/scholar?q=Ossama+Obeid%2C+Wajdi+Zaghouani%2C+Behrang+Mohit%2C+Nizar+Habash%2C+Kemal+Oflazer%2C+and+Nadi+Tomeh%2E+2013%2E+A+web%2Dbased+annotation+framework+for+large%2Dscale+text+correction%2E+In+Sixth+International+Joint+Conferen)
21. Ossama Obeid, , Houda Bouamor, Wajdi Zaghouani, Mahmoud Ghoneim, Abdelati Hawwari, Mona Diab, and Kemal Oflazer. 2016. Mandiac: A web-based annotation system for manual arabic diacritization. In Proceedings of the International Conference on Language Resources and Evaluation (LREC-2016) Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools (OSACT2). [find](https://scholar.google.com/scholar?q=Ossama+Obeid%2C+%2C+Houda+Bouamor%2C+Wajdi+Zaghouani%2C+Mahmoud+Ghoneim%2C+Abdelati+Hawwari%2C+Mona+Diab%2C+and+Kemal+Oflazer%2E+2016%2E+Mandiac%3A+A+web%2Dbased+annotation+system+for+manual+arabic+diacritization%2E+In+Proce)
22. Arfath Pasha, Mohamed Al-Badrashiny, Mona Diab, Ahmed El Kholy, Ramy Eskander, Nizar Habash, Manoj Pooleery, Owen Rambow, and Ryan Roth. 2014. [Madamira: A fast, comprehensive tool for morphological analysis and disambiguation of arabic](https://md.research.elamur.ai/free/en/papers/pasha-2014-madamira-fast-comprehensive-tool-morphological/). In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, May. European Language Resources Association (ELRA).
23. Massimo Poesio and Ron Artstein. 2005. Annotating (anaphoric) ambiguity. In In Proceedings of the Corpus Linguistics Conference. [find](https://scholar.google.com/scholar?q=Massimo+Poesio+and+Ron+Artstein%2E+2005%2E+Annotating+%28anaphoric%29+ambiguity%2E+In+In+Proceedings+of+the+Corpus+Linguistics+Conference%2E)
24. Popescu-Belis, 2007. Le role des metriques d’evaluation dans le processus de recherche en TAL, pages 67–91. Traitement Automatique de la Langue, vol. 48, n. 1. [find](https://scholar.google.com/scholar?q=Popescu%2DBelis%2C+2007%2E+Le+role+des+metriques+d%E2%80%99evaluation+dans+le+processus+de+recherche+en+TAL%2C+pages+67%E2%80%9391%2E+Traitement+Automatique+de+la+Langue%2C+vol%2E+48%2C+n%2E+1%2E)
25. Alexandr Rosen, Jirka Hana, Barbora Štindlová, and Anna Feldman. 2013. Evaluating and automating the annotation of a learner corpus. Language Resources and Evaluation, pages 1–28, April. [↗](https://doi.org/10.1007/s10579-013-9226-3)
26. Alla Rozovskaya and Dan Roth. 2010. Annotating esl errors: Challenges and rewards. In NAACL Workshop on Innovative Use of NLP for Building Educational Applications, Los Angeles, CA. [find](https://scholar.google.com/scholar?q=Alla+Rozovskaya+and+Dan+Roth%2E+2010%2E+Annotating+esl+errors%3A+Challenges+and+rewards%2E+In+NAACL+Workshop+on+Innovative+Use+of+NLP+for+Building+Educational+Applications%2C+Los+Angeles%2C+CA%2E)
27. Alla Rozovskaya, Houda Bouamor, Nizar Habash, Wajdi Zaghouani, Ossama Obeid, and Behrang Mohit. 2015. [The second qalb shared task on automatic text correction for arabic](https://md.research.elamur.ai/free/en/papers/rozovskaya-2015-second-qalb-shared-task-automatic/). In Proceedings of the ACL-IJCNLP Workshop on Arabic Natural Language Processing, page 26.
28. Josef Ruppenhofer, Russell Lee-Goldman, Caroline Sporleder, and Roser Morante. 2013. Beyond sentence-level semantic role labeling: linking argument structures in discourse. Language Resources and Evaluation, 47(3):695–721. [↗](https://doi.org/10.1007/s10579-012-9201-4)
29. Eckehard Schulz. 2004. A Student Grammar of Modern Standard Arabic. Cambridge University Press, Cambridge, United Kingdom. [find](https://scholar.google.com/scholar?q=Eckehard+Schulz%2E+2004%2E+A+Student+Grammar+of+Modern+Standard+Arabic%2E+Cambridge+University+Press%2C+Cambridge%2C+United+Kingdom%2E)
30. Anas Shahrour, Salam Khalifa, and Nizar Habash. 2015. [Improving arabic diacritization through syntactic analysis](https://md.research.elamur.ai/free/en/papers/shahrour-2015-improving-arabic-diacritization-through-syntactic/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1309–1315, Lisbon, Portugal.
31. Yannick Versley and Universitt Tbingen. 2006. Disagreement dissected: vagueness as a source of ambiguity in nominal (co-) reference. In In: Ambiguity in Anaphora Workshop Proceedings, pages 83–89. [find](https://scholar.google.com/scholar?q=Yannick+Versley+and+Universitt+Tbingen%2E+2006%2E+Disagreement+dissected%3A+vagueness+as+a+source+of+ambiguity+in+nominal+%28co%2D%29+reference%2E+In+In%3A+Ambiguity+in+Anaphora+Workshop+Proceedings%2C+pages+83%E2%80%9389%2E)
32. Wajdi Zaghouani, Mona Diab, Aous Mansouri, Sameer Pradhan, and Martha Palmer. 2010. The revised arabic propbank. In Proceedings of the Association for Computational Linguistics Fourth Linguistic Annotation Workshop, pages 222–226. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Wajdi+Zaghouani%2C+Mona+Diab%2C+Aous+Mansouri%2C+Sameer+Pradhan%2C+and+Martha+Palmer%2E+2010%2E+The+revised+arabic+propbank%2E+In+Proceedings+of+the+Association+for+Computational+Linguistics+Fourth+Linguistic+Annot)
33. Wajdi Zaghouani, Abdelati Hawwari, and Mona Diab. 2012. A pilot propbank annotation for quranic arabic. In Proceedings of the Fourth Workshop on Computational Linguistics for Literature co-located with the North American Association Computational Linguistics conference (NAACL-HLT 2012, page 78. [find](https://scholar.google.com/scholar?q=Wajdi+Zaghouani%2C+Abdelati+Hawwari%2C+and+Mona+Diab%2E+2012%2E+A+pilot+propbank+annotation+for+quranic+arabic%2E+In+Proceedings+of+the+Fourth+Workshop+on+Computational+Linguistics+for+Literature+co%2Dlocated+wit)
34. Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Ossama Obeid, Nadi Tomeh, Alla Rozovskaya, Noura Farra, Sarah Alkuhlani, and Kemal Oflazer. 2014. [Large scale arabic error annotation: Guidelines and framework](https://md.research.elamur.ai/free/en/papers/zaghouani-2014-large-scale-arabic-error-annotation/). In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), Reykjavik, Iceland, May 26-31, 2014., pages 2362–2369. [↗](https://doi.org/10.13140/rg.2.1.3273.6169)
35. Wajdi Zaghouani, Nizar Habash, Houda Bouamor, Alla Rozovskaya, Behrang Mohit, Abeer Heider, and Kemal Oflazer. 2015. [Correction annotation for non-native arabic texts: Guidelines and corpus](https://md.research.elamur.ai/free/en/papers/zaghouani-2015-correction-annotation-non-native-arabic/). In Proceedings of the Association for Computational Linguistics Fourth Linguistic Annotation Workshop, pages 129–139.
36. Wajdi Zaghouani, Houda Bouamor, Abdelati Hawwari, Mona Diab, Ossama Obeid, Mahmoud Ghoneim, Sawsan Alqahtani, and Kemal Oflazer. 2016a. [Guidelines and framework for a large scale arabic diacritized corpus](https://md.research.elamur.ai/free/en/papers/zaghouani-2016-guidelines-framework-large-scale-arabic/). In The Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 3637–3643. European Language Resources Association (ELRA).
37. Wajdi Zaghouani, Nizar Habash, Ossama Obeid, Behrang Mohit, Houda Bouamor, and Kemal Oflazer. 2016b. [Building an arabic machine translation post-edited corpus: Guidelines and annotation](https://md.research.elamur.ai/free/en/papers/zaghouani-2016-building-arabic-machine-translation-post/). In International Conference on Language Resources and Evaluation (LREC 2016).
38. Imed Zitouni, Jeffrey S. Sorensen, and Ruhi Sarikaya. 2006. [Maximum entropy based restoration of arabic diacritics](https://md.research.elamur.ai/free/en/papers/zitouni-2006-maximum-entropy-based-restoration-arabic/). In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 577–584, Sydney, Australia.

*Updated 1 November 2026*