---
title: "A Morphologically Annotated Corpus of Emirati Arabic (2018)"
description: "The authors describe the guidelines and their annotation interface, and they track quality with a… 48 references, cited by 35 papers here."
authors: ["Salam Khalifa", "Nizar Habash", "Fadhl Eryani", "Ossama Obeid", "Dana Abdulrahim", "Meera Al Kaabi"]
year: 2018
venue: "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)"
source: "https://aclanthology.org/L18-1607/"
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/khalifa-2018-morphologically-annotated-corpus-emirati-arabic/"
---
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# A Morphologically Annotated Corpus of Emirati Arabic

**Authors:** [Salam Khalifa](https://md.research.elamur.ai/free/en/authors/salam-khalifa/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Fadhl Eryani](https://md.research.elamur.ai/free/en/authors/fadhl-eryani/), [Ossama Obeid](https://md.research.elamur.ai/free/en/authors/ossama-obeid/), [Dana Abdulrahim](https://md.research.elamur.ai/free/en/authors/dana-abdulrahim/) and [Meera Al Kaabi](https://md.research.elamur.ai/free/en/authors/meera-al-kaabi/) · **Year:** 2018 · **Venue:** [Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2018](https://md.research.elamur.ai/free/en/venues/lrec/2018/)) · **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/L18-1607/) · [PDF](https://aclanthology.org/L18-1607.pdf) · [OpenAlex](https://openalex.org/W2805431570)

**Citations:** 38 citations (OpenAlex, 2026-10-14) · [35](https://md.research.elamur.ai/free/en/papers/khalifa-2018-morphologically-annotated-corpus-emirati-arabic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Dialectal Arabic is the variety people actually speak and increasingly write online, yet its NLP resources lag well behind those for Modern Standard Arabic (MSA). MSA tools perform poorly on dialects, and Gulf Arabic has received less attention than Egyptian or Levantine. The authors address this with an ongoing project to manually annotate about 200,000 words of Emirati Arabic. The text comes from the Gumar corpus, a large collection of online conversational novels. Each word receives six layers of annotation:

- spelling normalized to CODA* conventions,
- D3-style clitic tokenization,
- POS,
- lemma,
- English gloss,
- dialect label.

The guidelines descend from the Penn Arabic Treebank tradition. The authors describe the guidelines and their annotation interface, and they track quality with a dedicated inter-annotator agreement subset. The result, the Annotated Gumar Corpus, is described as the first large-scale, manually built morphological resource for Emirati Arabic. It is intended to support taggers, morphological disambiguators and downstream tasks such as machine translation for an under-resourced dialect.

## Key points
- The source is the Gumar corpus: over 100 million words in six Gulf varieties. At the document level, more than 90% of it is Gulf Arabic, and about 11% is Emirati.
- The Annotated Gumar Corpus has 200,110 tokens in 15,277 sentences, averaging about 13 words per sentence. It takes roughly the first 25,000 words (cut at a sentence boundary) from each of eight novels by eight different pen-named authors.
- An extra ~12,000 words (about 1,500 per novel) are set aside for inter-annotator agreement, bringing the total to roughly 212,000 words.
- Spelling follows the revised CODA* rules (consonant mapping, vowel spelling, affixation and cliticization).
- Tokenization uses the D3 scheme. All clitics, including the definite article Al, are split off the baseword, as in b+Al+[jwς] 'with the hunger'.
- Before this effort, the whole Gumar corpus had only automatic analysis, produced by MADAMIRA in Egyptian mode.

## Interesting ideas
- Drawing a fixed-size opening slice from many different authors, rather than one large block, spreads coverage across writing styles at no extra cost.
- The agreement subset is sampled from every part and checked repeatedly while annotation is under way, not only once at the end.
- Spelling normalization is a separate annotation layer that maps variant spellings to one convention, while the raw form stays available.
- A single, explicitly defined segmentation rule separates every attachable clitic from a clearly identified baseword.
- Each word carries a variety/dialect tag as part of the same annotation pass.

## For Mova

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

- **I1** (learn): Gold and silver evaluation sets could be capped per source text or author, so parser scores reflect stylistic breadth rather than one narrator's habits.
- **I2** (learn): Witness-parser agreement and LLM-teacher consistency could be re-measured on a fixed cross-section after each training round, to catch drift early.
- **I3** (tokens): Informal or nonstandard English spellings could map to a canonical form for lemma lookup, with the original surface string kept so each step stays explainable.
- **I4** (tokens): Contractions and possessive 's could be split under one documented rule that matches UD tokenization, so tagger and parser always see the same units.
- **I5** (tree): Tokens could carry a register or variety label (e.g., archaic fable language vs. modern prose), letting the parser or later stages choose appropriate handling for unusual forms.

*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

- [New York University Abu Dhabi](https://md.research.elamur.ai/free/en/universities/new-york-university-abu-dhabi/) — University, Abu Dhabi, United Arab Emirates · 122 works · 4,602 citations
- [United Arab Emirates University](https://md.research.elamur.ai/free/en/universities/united-arab-emirates-university/) — University, Al Ain, United Arab Emirates · 4 works · 89 citations
- [University of Bahrain](https://md.research.elamur.ai/free/en/universities/university-of-bahrain/) — University, Isa Town, Bahrain · 4 works · 271 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 · [🧩 Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · 405 works

## Cite

Cite as: Salam Khalifa, Nizar Habash, Fadhl Eryani, Ossama Obeid, Dana Abdulrahim and Meera Al Kaabi. 2018. A Morphologically Annotated Corpus of Emirati Arabic. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).

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

```bibtex
@inproceedings{khalifa2018morphologically,
  title = {{A Morphologically Annotated Corpus of Emirati Arabic}},
  author = {Salam Khalifa, Nizar Habash, Fadhl Eryani, Ossama Obeid, Dana Abdulrahim and Meera {Al Kaabi}},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/4ujvhk7xskc6},
  url = {https://doi.org/10.63317/4ujvhk7xskc6}
}
```

- [← previous NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs](https://md.research.elamur.ai/free/en/papers/baziotis-2018-ntua-slp-semeval-2018-task-s18-1100/)
- [next → EMA at SemEval-2018 Task 1: Emotion Mining for Arabic](https://md.research.elamur.ai/free/en/papers/badaro-2018-ema-semeval-2018-task-1/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/4/)

## References

48 references, 21 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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*Updated 1 November 2026*