---
title: "Building Arabic NLP from the Ground Up: Twenty Years of"
description: "This position paper looks back on two decades of an Arabic NLP research program, asking what building infrastructure for a… 53 references."
authors: ["Wajdi Zaghouani"]
year: 2026
venue: "Proceedings of The Big Picture v2: Crafting a Research Narrative"
source: "https://aclanthology.org/2026.bigpicture-main.8/"
doi: "10.18653/v1/2026.bigpicture-main.8"
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-2026-building-arabic-nlp-ground-up/"
---
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# Building Arabic NLP from the Ground Up: Twenty Years of Lessons, Failures, and Open Problems

**Authors:** [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/) · **Year:** 2026 · **Venue:** [Proceedings of The Big Picture v2: Crafting a Research Narrative](https://md.research.elamur.ai/free/en/venues/bigpicture/) ([Big Picture v2: Crafting a Research Narrative 2026](https://md.research.elamur.ai/free/en/venues/bigpicture/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bigpicture-main.8/) · [PDF](https://aclanthology.org/2026.bigpicture-main.8.pdf) · [DOI 10.18653/v1/2026.bigpicture-main.8](https://doi.org/10.18653/v1/2026.bigpicture-main.8) · [OpenAlex](https://openalex.org/W7166809743)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This position paper looks back on two decades of an Arabic NLP research program, asking what building infrastructure for a large but under-resourced language taught its author. From roughly 2004 to 2014 the work centred on core resources such as the Arabic Treebank, the Arabic PropBank, named entity recognition, error correction corpora and dialect data. From the mid-2010s it broadened into social media analysis and computational social science, covering hate speech, misinformation, mental health and political framing. Instead of listing outputs, the author draws out three lessons. Datasets gain value mainly through the communities built around them. Shared tasks act as tools that force agreement on how problems are defined. Social-science work demands skills that standard NLP training lacks. Three failures are also examined: a depression detection corpus that never entered clinical use, too many shared tasks pursued too thinly, and the mistaken belief that Modern Standard Arabic resources would carry over to dialects. Its central, transferable claim is that the hardest obstacles for underserved languages are social, institutional and epistemic, not linguistic.

## Key points
- **Two phases:** foundational resources came first (2004–2014), including the Arabic Treebank, Arabic PropBank, NER systems, morphological resources and dialect corpora. A shift toward socially oriented applications followed from the mid-2010s.
- **QALB as a case study:** the Qatar Arabic Language Bank error correction corpus had real impact mainly through its shared tasks at EMNLP 2014 and ACL 2015. The same dataset-plus-task pattern recurred with AraP-Tweet (FIRE 2019 author profiling), ADHAR, MAHED and ImageEval 2025.
- **Community over individual resources:** WANLP, co-organized since 2014, is presented as a lasting community whose value exceeds that of any single resource. The CheckThat! Lab shows how annotation disputes expose conceptual ambiguity in tasks such as claim check-worthiness.
- **Vision-level mistake:** the author assumed that better tools would eventually reach social scientists and policymakers. In reality, that kind of impact requires deliberate engagement that academic incentives seldom reward.
- **Ethics as a core design constraint:** the paper covers privacy, mental-health harms, annotator wellbeing, dialect and cultural bias, and dual-use risks such as surveillance or censorship.

*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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- [Northwestern University in Qatar](https://md.research.elamur.ai/free/en/universities/northwestern-university-in-qatar/) — University, Doha, Qatar · 39 works · 148 citations

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## Cite

Cite as: Wajdi Zaghouani. 2026. Building Arabic NLP from the Ground Up: Twenty Years of Lessons, Failures, and Open Problems. Proceedings of The Big Picture v2: Crafting a Research Narrative.

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

```bibtex
@inproceedings{zaghouani2026building,
  title = {{Building Arabic NLP from the Ground Up: Twenty Years of Lessons, Failures, and Open Problems}},
  author = {Wajdi Zaghouani},
  booktitle = {Proceedings of The Big Picture v2: Crafting a Research Narrative},
  year = {2026},
  doi = {10.18653/v1/2026.bigpicture-main.8},
  url = {https://doi.org/10.18653/v1/2026.bigpicture-main.8}
}
```

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## References

53 references, 13 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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34. Shestakov, A. and Zaghouani, W. (2024). Analyzing Conflict Through Data: A Dataset on the Digital Framing of Sheikh Jarrah Evictions. In Proceedings of the Second Workshop on NLP for Political Sciences, LREC-COLING 2024, pages 55–67. [find](https://scholar.google.com/scholar?q=Shestakov%2C+A%2E+and+Zaghouani%2C+W%2E+%282024%29%2E+Analyzing+Conflict+Through+Data%3A+A+Dataset+on+the+Digital+Framing+of+Sheikh+Jarrah+Evictions%2E+In+Proceedings+of+the+Second+Workshop+on+NLP+for+Political+Science)
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37. Nakov, P., Barrón-Cedeño, A., Elsayed, T., Suwaileh, R., Màrquez, L., Zaghouani, W., Atanasova, P., Kyuchukov, S., and Da San Martino, G. (2018). Overview of the CLEF-2018 CheckThat! Lab. In Lecture Notes in Computer Science. [find](https://scholar.google.com/scholar?q=Nakov%2C+P%2E%2C+Barr%C3%B3n%2DCede%C3%B1o%2C+A%2E%2C+Elsayed%2C+T%2E%2C+Suwaileh%2C+R%2E%2C+M%C3%A0rquez%2C+L%2E%2C+Zaghouani%2C+W%2E%2C+Atanasova%2C+P%2E%2C+Kyuchukov%2C+S%2E%2C+and+Da+San+Martino%2C+G%2E+%282018%29%2E+Overview+of+the+CLEF%2D2018+CheckThat%21+Lab%2E+In+Lecture+N)
38. Nakov, P., Barrón-Cedeño, A., Da San Martino, G., Alam, F., Struß, J. M., Mandl, T., Miguez, R., Caselli, T., Kutlu, M., and Zaghouani, W. (2022). The CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection. In Proceedings of ECIR 2022, pages 416–428. [find](https://scholar.google.com/scholar?q=Nakov%2C+P%2E%2C+Barr%C3%B3n%2DCede%C3%B1o%2C+A%2E%2C+Da+San+Martino%2C+G%2E%2C+Alam%2C+F%2E%2C+Stru%C3%9F%2C+J%2E+M%2E%2C+Mandl%2C+T%2E%2C+Miguez%2C+R%2E%2C+Caselli%2C+T%2E%2C+Kutlu%2C+M%2E%2C+and+Zaghouani%2C+W%2E+%282022%29%2E+The+CLEF%2D2022+CheckThat%21+Lab+on+Fighting+the+COVID%2D19)
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*Updated 2 November 2026*