---
title: "Building a Corpus for Palestinian Arabic: a Preliminary Study"
description: "Most Arabic NLP tools target Modern Standard Arabic (MSA). 32 references, cited by 39 papers here."
authors: ["Mustafa Jarrar", "Nizar Habash", "Diyam Akra", "Nasser Zalmout"]
year: 2014
venue: "Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)"
source: "https://aclanthology.org/W14-3603/"
doi: "10.3115/v1/w14-3603"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/jarrar-2014-building-corpus-palestinian-arabic-preliminary/"
---
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# Building a Corpus for Palestinian Arabic: a Preliminary Study

**Authors:** [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Diyam Akra](https://md.research.elamur.ai/free/en/authors/diyam-akra/) and [Nasser Zalmout](https://md.research.elamur.ai/free/en/authors/nasser-zalmout/) · **Year:** 2014 · **Venue:** [Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-on-arabic-natural-language-processing-anlp/) ([ANLP 2014](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-on-arabic-natural-language-processing-anlp/2014/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W14-3603/) · [PDF](https://aclanthology.org/W14-3603.pdf) · [DOI 10.3115/v1/w14-3603](https://doi.org/10.3115/v1/w14-3603) · [OpenAlex](https://openalex.org/W2117202778)

**Citations:** 57 citations (OpenAlex, 2026-10-14) · [39](https://md.research.elamur.ai/free/en/papers/jarrar-2014-building-corpus-palestinian-arabic-preliminary/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most Arabic NLP tools target Modern Standard Arabic (MSA). Spoken dialects differ from it in sound, morphology and vocabulary, have no agreed spelling, and appear in writing mostly as informal online chatter. This paper reports early work on Curras, an annotated corpus of Palestinian Arabic (PAL) of roughly 43K words drawn from varied sources. The authors first survey how PAL departs from MSA. Phonologically, /q/ and /k/ shift by sub-dialect and the glottal stop disappears. Morphologically, PAL has lost case and mood and adds new clitics such as progressive b+ and negation ma+…+š. Lexically, it borrows from Turkish, Persian, Italian, English, French and Hebrew. They then ask whether existing analyzers can speed up annotation, running two pilots: an MSA-oriented system and the Egyptian mode of MADAMIRA. Each output is graded, and annotators either accept a converted analysis or fix it. The Egyptian analyzer proves more useful than the MSA one but still misses PAL-specific words and forms. This motivates PAL-specific CODA spelling guidelines, a PAL lexicon and an extended analyzer.

## Key points
- Curras is a Palestinian Arabic corpus of about 43K words from diverse sources, with financial support from Palestine's higher education ministry.
- Each token is annotated with:
  - its raw form,
  - a CODA-normalized spelling,
  - a lemma,
  - a Buckwalter-style POS/segmentation,
  - an English gloss.
- In the pilots, automatic analyses (MSA-oriented and MADAMIRA-EGY) were graded as Correct, Usable, Wrong or NA (no analysis). The MSA output carries case endings that PAL no longer has.
- The final corpus records whether each entry is a validated tool conversion (MADA) or was edited or written by the annotator (ANNO).
- The closest prior dialect resource, the Levantine Arabic Treebank (LATB, about 26,000 words of Jordanian telephone speech), avoided spelling variation because it used transcripts, unlike Curras's written sources.
- Planned work includes linking lemmas to Arabic WordNet and releasing the corpus publicly for research.

## Interesting ideas
- Instead of a yes/no correctness check, tool output is graded on four levels (correct, usable with light edits, wrong, no analysis), which measures how much annotator effort a pre-annotator actually saves.
- Every annotated token keeps a provenance flag showing whether it was accepted from a tool or produced by a human.
- When no analyzer exists for a variety, borrowing one built for a closely related variety (Egyptian rather than MSA) gives a better starting point than the standard language.
- Raw spelling is kept as one layer and a conventionalized spelling (CODA) as a separate layer, so noisy input is normalized without losing the original.
- Lemmas are planned to link to an existing lexical ontology (Arabic WordNet), so corpus annotation connects directly to semantic resources.

## For Mova

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

- **I1** (learn): Silver-data evaluation could tag witness-parser outputs with the same four-way scale, so we can tell "needs a small repair rule" apart from "fundamentally wrong."
- **I2** (learn): Marking each silver arc or tag as machine-agreed, teacher-corrected or hand-made lets training weight sources differently and audit where errors originate.
- **I3** (tree): Bootstrapping parses for nonstandard English registers, such as dialectal or social-media text, could start from the treebank closest in register rather than from newswire.
- **I4** (tokens): The tokenizer could store a normalized form beside the surface form for misspellings and informal spellings, keeping explanations traceable to the actual text.
- **I5** (world): Attaching WordNet sense IDs to lemmas during annotation would give the world-building stage typed entities without a separate linking pass.

*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](https://md.research.elamur.ai/free/en/universities/new-york-university/) — University, New York, United States · 201 works · 100,180 citations
- [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
- [Birzeit University](https://md.research.elamur.ai/free/en/universities/birzeit-university/) — University, Birzeit, Palestine · 36 works · 798 citations

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## Cite

Cite as: Mustafa Jarrar, Nizar Habash, Diyam Akra and Nasser Zalmout. 2014. Building a Corpus for Palestinian Arabic: a Preliminary Study. Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP).

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

```bibtex
@inproceedings{jarrar2014building,
  title = {{Building a Corpus for Palestinian Arabic: a Preliminary Study}},
  author = {Mustafa Jarrar, Nizar Habash, Diyam Akra and Nasser Zalmout},
  booktitle = {Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)},
  year = {2014},
  doi = {10.3115/v1/w14-3603},
  url = {https://doi.org/10.3115/v1/w14-3603}
}
```

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## References

32 references, 14 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*