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# Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging

**Authors:** Hassan Sajjad; Fahim Dalvi; [Nadir Durrani](https://md.mova.elamur.ai/free/en/docs/papers/authors/nadir-durrani/); [Ahmed Abdelali](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/); [Yonatan Belinkov](https://md.mova.elamur.ai/free/en/docs/papers/authors/yonatan-belinkov/); [Stephan Vogel](https://md.mova.elamur.ai/free/en/docs/papers/authors/stephan-vogel/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) **Link:** [aclanthology.org/P17-2095](https://aclanthology.org/P17-2095/) · library `aclbulk-2017-sajjad-challenging-language-dependent-segmentation-arabic` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**13** citations](https://openalex.org/works?filter=cites:W2962724530) openalex · 2026-10-18[**13** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2017-sajjad-challenging-language-dependent-segmentation-arabic/) of the 140673 papers whose bibliographies we read[Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/P17-2/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/P17-2095/)
- [PDF](https://aclanthology.org/P17-2095.pdf)
- [DOI 10.18653/v1/p17-2095](https://doi.org/10.18653/v1/p17-2095)
- [OpenAlex](https://openalex.org/W2962724530)

**Authors (6)**

1. [Hassan Sajjad](https://openalex.org/A5042954793) [ORCID](https://orcid.org/0000-0002-8584-6595) · h 33 · 3425 citations · 124 works
2. [Fahim Dalvi](https://openalex.org/A5002245931) [ORCID](https://orcid.org/0000-0003-1183-7837) · h 23 · 2130 citations · 99 works
3. [Nadir Durrani](https://openalex.org/A5032689067) [ORCID](https://orcid.org/0000-0002-9378-4128) · h 32 · 3403 citations · 145 works
4. [Ahmed Abdelali](https://openalex.org/A5047182137) [ORCID](https://orcid.org/0000-0002-4160-8181) · h 25 · 2329 citations · 137 works
5. [Yonatan Belinkov](https://openalex.org/A5051184573) [ORCID](https://orcid.org/0000-0002-6280-5045) · h 45 · 8439 citations · 273 works
6. [Stephan Vogel](https://openalex.org/A5064572063) [ORCID](https://orcid.org/0000-0003-2630-4881) · h 45 · 6904 citations · 233 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper asks whether Arabic can be segmented without a language-specific morphological tool such as MADAMIRA or Farasa. It compares three data-driven units (byte-pair encoding sub-words, pure characters, and word vectors built by a character CNN) on neural machine translation with about 1.2 million training sentences, in both directions, and on POS tagging of the Arabic Treebank (483,909 words, split 80/5/15). For Arabic-to-English, BPE scores 31.1 BLEU against 30.9 for morphological segmentation; for English-to-Arabic it scores 18.0 against 18.2. Character-based models are closest on POS tagging, at 95.9% accuracy, 1.3 points below morphological segmentation. The authors also find that the NMT system works best when the source/target token ratio is near 1 or above.

## Ideas

- I1. Measure, in `tools/hbo`, how well an unsupervised split (BPE with a fixed merge count, chosen on held-out data) agrees with the MACULA gold segmentation of Hebrew tokens. It is a control against the hand-written prefix and suffix tables (`lex`, `pre`). Report split agreement and the out-of-vocabulary rate, not only the final parse.
- I2. The paper finds that BPE cuts unknown words into known fragments that mislead the translation, and that applying BPE only to the known vocabulary helps. For `tools/hbo`: keep the table split for known forms, and use a plain fixed split only for forms the tables do not cover. Test it on held-out Torah tokens.
- I3. A value ablation: the gain of morphological segmentation over no segmentation (+4.6 BLEU Arabic-to-English) is a measured worth. Run the same comparison in `gparse`, segmented against unsegmented Hebrew input, and keep the segmenter only where it improves the parse.

## For Mova

I1 and I2 go into `tools/hbo` (the segmenter's tables and its fallback for out-of-vocabulary forms); the measure is split agreement against MACULA. I3 is a value ablation for `gparse` on the output of `tools/hbo`. The neural models are not reused: Mova does not need an NMT system to segment.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/p17-2095](https://doi.org/10.18653/v1/p17-2095)

```
@inproceedings{sajjad2017challenging,
  title = {{Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging}},
  author = {Sajjad, Hassan and Dalvi, Fahim and Durrani, Nadir and Abdelali, Ahmed and Belinkov, Yonatan and Vogel, Stephan},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-2095},
  url = {https://doi.org/10.18653/v1/p17-2095}
}
```

- [← previous Arabic Diacritization: Stats, Rules, and Hacks](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-darwish-arabic-diacritization-stats-rules-hacks/)
- [next → A Neural Architecture for Dialectal Arabic Segmentation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

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

1. text to base phrase chunks. In Proceedings of HLT-NAACL 2004: Short Papers. Stroudsburg, PA, USA, HLT-NAACL-Short ’04. [find](https://scholar.google.com/scholar?q=text+to+base+phrase+chunks%2E+In+Proceedings+of+HLT%2DNAACL+2004%3A+Short+Papers%2E+Stroudsburg%2C+PA%2C+USA%2C+HLT%2DNAACL%2DShort+%E2%80%9904%2E)
2. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. 2016. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. San Diego, California. [find](https://scholar.google.com/scholar?q=Ahmed+Abdelali%2C+Kareem+Darwish%2C+Nadir+Durrani%2C+and+Hamdy+Mubarak%2E+2016%2E+Farasa%3A+A+fast+and+furious+segmenter+for+arabic%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Asso) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-abdelali-farasa-fast-furious-segmenter-arabic/)
3. Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and Stephan Vogel. 2016. QCRI machine translation systems for IWSLT 16. In Proceedings of the 15th International Workshop on Spoken Language Translation. Seattle, WA, USA, IWSLT ’16. [find](https://scholar.google.com/scholar?q=Nadir+Durrani%2C+Fahim+Dalvi%2C+Hassan+Sajjad%2C+and+Stephan+Vogel%2E+2016%2E+QCRI+machine+translation+systems+for+IWSLT+16%2E+In+Proceedings+of+the+15th+International+Workshop+on+Spoken+Language+Translation%2E+Sea)
4. Amjad Almahairi, Cho Kyunghyun, Nizar Habash, and Aaron Courville. 2016. First result on Arabic neural machine translation. In https://arxiv.org/abs/1606.02680. [↗](https://arxiv.org/abs/1606.02680)
5. Nadir Durrani, Hassan Sajjad, Alexander Fraser, and Helmut Schmid. 2010. Hindi-to-Urdu Machine Translation through Transliteration. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Uppsala, Sweden, pages 465– 474. http://www.aclweb.org/anthology/P10-1048. [↗](https://aclanthology.org/P10-1048/)
6. Amittai Axelrod, Xiaodong He, and Jianfeng Gao. 2011. Domain adaptation via pseudo in-domain data selection. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, United Kingdom, EMNLP ’11. [find](https://scholar.google.com/scholar?q=Amittai+Axelrod%2C+Xiaodong+He%2C+and+Jianfeng+Gao%2E+2011%2E+Domain+adaptation+via+pseudo+in%2Ddomain+data+selection%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E+Edinb)
7. Nadir Durrani, Hassan Sajjad, Hieu Hoang, and Philipp Koehn. 2014. Integrating an unsupervised transliteration model into statistical machine translation. In Proceedings of the 15th Conference of the European Chapter of the ACL (EACL 2014). Gothenburg, Sweden. [find](https://scholar.google.com/scholar?q=Nadir+Durrani%2C+Hassan+Sajjad%2C+Hieu+Hoang%2C+and+Philipp+Koehn%2E+2014%2E+Integrating+an+unsupervised+transliteration+model+into+statistical+machine+translation%2E+In+Proceedings+of+the+15th+Conference+of+the+)
8. Kenneth R Beesley. 1996. Arabic finite-state morphological analysis and generation. In ACL. pages 89– 94. [find](https://scholar.google.com/scholar?q=Kenneth+R+Beesley%2E+1996%2E+Arabic+finite%2Dstate+morphological+analysis+and+generation%2E+In+ACL%2E+pages+89%E2%80%93+94%2E)
9. Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017. What do Neural Machine Translation Models Learn about Morphology? In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Vancouver, Canada. [find](https://scholar.google.com/scholar?q=Yonatan+Belinkov%2C+Nadir+Durrani%2C+Fahim+Dalvi%2C+Hassan+Sajjad%2C+and+James+Glass%2E+2017%2E+What+do+Neural+Machine+Translation+Models+Learn+about+Morphology%3F+In+Proceedings+of+the+55th+Annual+Meeting+of+the+A)
10. Ahmed El Kholy and Nizar Habash. 2012. Orthographic and morphological processing for English– Arabic statistical machine translation. Machine Translation 26(1-2). [find](https://scholar.google.com/scholar?q=Ahmed+El+Kholy+and+Nizar+Habash%2E+2012%2E+Orthographic+and+morphological+processing+for+English%E2%80%93+Arabic+statistical+machine+translation%2E+Machine+Translation+26%281%2D2%29%2E)
11. Mark Fishel and Harri Kirik. 2010. Linguistically motivated unsupervised segmentation for machine translation. In In Proceedings of the seventh International Conference on Language Resources and Evaluation (LREC. [find](https://scholar.google.com/scholar?q=Mark+Fishel+and+Harri+Kirik%2E+2010%2E+Linguistically+motivated+unsupervised+segmentation+for+machine+translation%2E+In+In+Proceedings+of+the+seventh+International+Conference+on+Language+Resources+and+Evalu)
12. Yonatan Belinkov and James Glass. 2016. Arabic and hebrew: Available corpora and initial results. In Proceedings of the Workshop on Semitic Machine Translation. [find](https://scholar.google.com/scholar?q=Yonatan+Belinkov+and+James+Glass%2E+2016%2E+Arabic+and+hebrew%3A+Available+corpora+and+initial+results%2E+In+Proceedings+of+the+Workshop+on+Semitic+Machine+Translation%2E)
13. Philip Gage. 1994. A new algorithm for data compression. C Users J. 12(2):23–38. [http://dl.acm.org/citation.cfm?id=177910.177914](http://dl.acm.org/citation.cfm?id=177910.177914). [↗](http://dl.acm.org/citation.cfm?id=177910.177914)
14. Thorsten Brants. 2000. Tnt: A statistical part-of-speech tagger. In Proceedings of the Sixth Conference on Applied Natural Language Processing. ANLC ’00, pages 224–231. https://doi.org/10.3115/974147.974178. [↗](https://doi.org/10.3115/974147.974178) [our card](https://md.mova.elamur.ai/free/en/docs/papers/brants-2000-tnt-statistical-part-speech-tagger/)
15. Francisco Guzmán, Hassan Sajjad, Stephan Vogel, and Ahmed Abdelali. 2013. The AMARA corpus: Building resources for translating the web’s educational content. In Proceedings of the 10th International Workshop on Spoken Language Technology (IWSLT-13). [find](https://scholar.google.com/scholar?q=Francisco+Guzma%CC%81n%2C+Hassan+Sajjad%2C+Stephan+Vogel%2C+and+Ahmed+Abdelali%2E+2013%2E+The+AMARA+corpus%3A+Building+resources+for+translating+the+web%E2%80%99s+educational+content%2E+In+Proceedings+of+the+10th+International+)
16. Mauro Cettolo, Christian Girardi, and Marcello Federico. 2012. WIT 3 : Web Inventory of Transcribed and Translated Talks. In Proceedings of the 16th Conference of the European Association for Machine Translation (EAMT). Trento, Italy, pages 261– 268. [find](https://scholar.google.com/scholar?q=Mauro+Cettolo%2C+Christian+Girardi%2C+and+Marcello+Federico%2E+2012%2E+WIT+3+%3A+Web+Inventory+of+Transcribed+and+Translated+Talks%2E+In+Proceedings+of+the+16th+Conference+of+the+European+Association+for+Machine+)
17. Nizar Habash and Owen Rambow. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA, ACL ’05. [find](https://scholar.google.com/scholar?q=Nizar+Habash+and+Owen+Rambow%2E+2005%2E+Arabic+tokenization%2C+part%2Dof%2Dspeech+tagging+and+morphological+disambiguation+in+one+fell+swoop%2E+In+Proceedings+of+the+43rd+Annual+Meeting+on+Association+for+Computa) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-habash-arabic-tokenization-part-speech-tagging/)
18. Marta R. Costa-jussà and José A. R. Fonollosa. 2016. Character-based neural machine translation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Berlin, Germany, pages 357–361. http://anthology.aclweb.org/P16-2058. [↗](https://aclanthology.org/P16-2058/)
19. Nizar Habash, Ryan Roth, Owen Rambow, Ramy Esk, and Nadi Tomeh. 2013. Morphological analysis and disambiguation for dialectal arabic. In In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). [find](https://scholar.google.com/scholar?q=Nizar+Habash%2C+Ryan+Roth%2C+Owen+Rambow%2C+Ramy+Esk%2C+and+Nadi+Tomeh%2E+2013%2E+Morphological+analysis+and+disambiguation+for+dialectal+arabic%2E+In+In+Proceedings+of+the+2013+Conference+of+the+North+American+Cha)
20. Vera Demberg. 2007. A language independent unsupervised model for morphological segmentation. In In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. [find](https://scholar.google.com/scholar?q=Vera+Demberg%2E+2007%2E+A+language+independent+unsupervised+model+for+morphological+segmentation%2E+In+In+Proceedings+of+the+45th+Annual+Meeting+of+the+Association+of+Computational+Linguistics%2E)
21. Nizar Habash and Fatiha Sadat. 2006. Arabic preprocessing schemes for statistical machine translation. In Proceedings of the Human Language Technology Conference of the North American Chapter of Mona Diab, Kadri Hacioglu, and Daniel Jurafsky. 2004. Automatic tagging of arabic text: From raw spoken language translation. In Proceedings of the 10th International Workshop on Spoken Language Technology (IWSLT-13). the Association for Computational Linguistics (HLT-NAACL’06). New York, NY, USA. [find](https://scholar.google.com/scholar?q=Nizar+Habash+and+Fatiha+Sadat%2E+2006%2E+Arabic+preprocessing+schemes+for+statistical+machine+translation%2E+In+Proceedings+of+the+Human+Language+Technology+Conference+of+the+North+American+Chapter+of+Mona+)
22. Salam Khalifa, Nasser Zalmout, and Nizar Habash. 2016. Yamama: Yet another multi-dialect arabic morphological analyzer. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations pages 223–227. [find](https://scholar.google.com/scholar?q=Salam+Khalifa%2C+Nasser+Zalmout%2C+and+Nizar+Habash%2E+2016%2E+Yamama%3A+Yet+another+multi%2Ddialect+arabic+morphological+analyzer%2E+Proceedings+of+COLING+2016%2C+the+26th+International+Conference+on+Computational+L)
23. Peter Smit Stig-Arne Grnroos Sami Virpioja and Mikko Kurimo. 2013. Morfessor 2.0: Python implementation and extensions for morfessor baseline. In Technical Report, Aalto University publication series SCIENCE + TECHNOLOGY. [find](https://scholar.google.com/scholar?q=Peter+Smit+Stig%2DArne+Grnroos+Sami+Virpioja+and+Mikko+Kurimo%2E+2013%2E+Morfessor+2%2E0%3A+Python+implementation+and+extensions+for+morfessor+baseline%2E+In+Technical+Report%2C+Aalto+University+publication+series+)
24. Yoon Kim. 2016. Seq2seq-attn. [https://](https://) github.com/harvardnlp/seq2seq-attn. [↗](https://)
25. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany. [find](https://scholar.google.com/scholar?q=Rico+Sennrich%2C+Barry+Haddow%2C+and+Alexandra+Birch%2E+2016%2E+Neural+machine+translation+of+rare+words+with+subword+units%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Ling)
26. Yoon Kim, Yacine Jernite, David Sontag, and Alexander Rush. 2016. Character-Aware Neural Language Models. In AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Yoon+Kim%2C+Yacine+Jernite%2C+David+Sontag%2C+and+Alexander+Rush%2E+2016%2E+Character%2DAware+Neural+Language+Models%2E+In+AAAI+Conference+on+Artificial+Intelligence%2E)
27. David Stallard, Jacob Devlin, Michael Kayser, Yoong Keok Lee, and Regina Barzilay. 2012. Unsupervised morphology rivals supervised morphology for arabic mt. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2. ACL ’12. [find](https://scholar.google.com/scholar?q=David+Stallard%2C+Jacob+Devlin%2C+Michael+Kayser%2C+Yoong+Keok+Lee%2C+and+Regina+Barzilay%2E+2012%2E+Unsupervised+morphology+rivals+supervised+morphology+for+arabic+mt%2E+In+Proceedings+of+the+50th+Annual+Meeting+o) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-stallard-unsupervised-morphology-rivals-supervised-morphology/)
28. John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning. ICML ’01, pages 282–289. [find](https://scholar.google.com/scholar?q=John+D%2E+Lafferty%2C+Andrew+McCallum%2C+and+Fernando+C%2E+N%2E+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+Proceedings+of+the+Eighteenth+Interna)
29. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, ukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144. http://arxiv.org/abs/1609.08144. [↗](https://arxiv.org/abs/1609.08144)
30. Wang Ling, Isabel Trancoso, Chris Dyer, and Alan W. Black. 2015. Character-based neural machine translation. CoRR abs/1511.04586. http://arxiv.org/abs/1511.04586. [↗](https://arxiv.org/abs/1511.04586)
31. Wang Ling, Isabel Trancoso, Chris Dyer, and Alan W. Black. 2016. Character-based neural machine translation. arXiv preprint arXiv:1511.04586 . [↗](https://arxiv.org/abs/1511.04586)
32. O. Frieder M. Aljlayl and D. Grossman. 2002. On Arabic-English cross-language information retrieval: A machine translation approach. In IEEE Third International Conference on Information Technology: Coding and Computing (ITCC). [find](https://scholar.google.com/scholar?q=O%2E+Frieder+M%2E+Aljlayl+and+D%2E+Grossman%2E+2002%2E+On+Arabic%2DEnglish+cross%2Dlanguage+information+retrieval%3A+A+machine+translation+approach%2E+In+IEEE+Third+International+Conference+on+Information+Technology%3A+C)
33. Ann Bies Seth Kulick Sondos Krouna Fatma Gaddeche Wajdi Zaghouani Mohamed Maamouri. 2010. Arabic treebank: Part 3 v 3.2 ldc2010t08. web download. Philadelphia: Linguistic Data Consortium . [find](https://scholar.google.com/scholar?q=Ann+Bies+Seth+Kulick+Sondos+Krouna+Fatma+Gaddeche+Wajdi+Zaghouani+Mohamed+Maamouri%2E+2010%2E+Arabic+treebank%3A+Part+3+v+3%2E2+ldc2010t08%2E+web+download%2E+Philadelphia%3A+Linguistic+Data+Consortium+%2E)
34. Preslav Nakov and Jörg Tiedemann. 2012. Combining word-level and character-level models for machine translation between closely-related languages. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Jeju, Korea, ACL ’12, pages 301– 305. [find](https://scholar.google.com/scholar?q=Preslav+Nakov+and+Jo%CC%88rg+Tiedemann%2E+2012%2E+Combining+word%2Dlevel+and+character%2Dlevel+models+for+machine+translation+between+closely%2Drelated+languages%2E+In+Proceedings+of+the+50th+Annual+Meeting+of+the+Ass)
35. Arfath Pasha, Mohamed Al-Badrashiny, Mona Diab, Ahmed El Kholy, Ramy Eskander, Nizar Habash, Manoj Pooleery, Owen Rambow, and Ryan M Roth. 2014. MADAMIRA: A fast, comprehensive tool for morphological analysis and disambiguation of Arabic. In Proceedings of the Language Resources and Evaluation Conference. Reykjavik, Iceland, LREC ’14, pages 1094–1101. [find](https://scholar.google.com/scholar?q=Arfath+Pasha%2C+Mohamed+Al%2DBadrashiny%2C+Mona+Diab%2C+Ahmed+El+Kholy%2C+Ramy+Eskander%2C+Nizar+Habash%2C+Manoj+Pooleery%2C+Owen+Rambow%2C+and+Ryan+M+Roth%2E+2014%2E+MADAMIRA%3A+A+fast%2C+comprehensive+tool+for+morphological+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2014-pasha-madamira-fast-comprehensive-tool-morphological/)
36. Hassan Sajjad, Francisco Guzmn, Preslav Nakov, Ahmed Abdelali, Kenton Murray, Fahad Al Obaidli, and Stephan Vogel. 2013. QCRI at IWSLT 2013: Experiments in Arabic-English and English-Arabic [find](https://scholar.google.com/scholar?q=Hassan+Sajjad%2C+Francisco+Guzmn%2C+Preslav+Nakov%2C+Ahmed+Abdelali%2C+Kenton+Murray%2C+Fahad+Al+Obaidli%2C+and+Stephan+Vogel%2E+2013%2E+QCRI+at+IWSLT+2013%3A+Experiments+in+Arabic%2DEnglish+and+English%2DArabic)