---
title: "Generating acceptable Arabic Core Vocabularies and Symbols"
description: "The authors are building a bilingual Arabic Symbol Dictionary for AAC users, and this paper describes how they assembled its… 25 references."
authors: ["E.A. Draffan", "Mike Wald", "Nawar Halabi", "Ouadie Sabia", "Wajdi Zaghouani", "Amatullah Kadous", "Amal Idris", "Nadine Zeinoun", "David Banes", "Dana Lawand"]
year: 2015
venue: "Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies"
source: "https://aclanthology.org/W15-5116/"
doi: "10.18653/v1/w15-5116"
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/draffan-2015-generating-acceptable-arabic-core-vocabularies/"
---
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# Generating acceptable Arabic Core Vocabularies and Symbols for AAC users

**Authors:** E.A. Draffan, [Mike Wald](https://md.research.elamur.ai/free/en/authors/mike-wald/), [Nawar Halabi](https://md.research.elamur.ai/free/en/authors/nawar-halabi/), Ouadie Sabia, [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/), Amatullah Kadous, Amal Idris, Nadine Zeinoun, David Banes and Dana Lawand · **Year:** 2015 · **Venue:** Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies · **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/W15-5116/) · [PDF](https://aclanthology.org/W15-5116.pdf) · [DOI 10.18653/v1/w15-5116](https://doi.org/10.18653/v1/w15-5116) · [OpenAlex](https://openalex.org/W2250654961)

**Citations:** 5 citations (OpenAlex, 2026-10-14)

## Summary
Therapists and teachers in the Arabic-speaking Gulf often rely on Western symbol sets and English core vocabularies, which fit poorly with Arabic language and culture. The authors are building a bilingual Arabic Symbol Dictionary for AAC users, and this paper describes how they assembled its core vocabulary. In Doha, they collected word lists from AAC users, their families, therapists and teachers, and ran a participatory forum of 63 people. The lists were combined into a seed vocabulary of about 500 entries. A vocabulary list analyser then compared this seed list with several Arabic frequency and reading lists. It produced three outputs: seed words with no external support, shared words scored per source, and external words absent from the seed, which become candidates. An expert makes the final choice in every case. Newly designed symbols are accepted through criteria-based voting in an online management system. A key finding is that Arabic AAC lists contain far more nouns than English core lists. This calls into question the direct transfer of English core-vocabulary assumptions and argues for personalised, context-sensitive vocabularies.

## Key points
- Seed list ("List a"): about 100 localised high-use words and multiwords, plus roughly 400 more. Therapists judged 500 to be the practical maximum for most users. Colloquial Qatari words and place names were included.
- Comparison sources ("Lists b"): the Kelly Project, 101languages.net's 1000 common spoken words, Aljazeera comments (colloquial), SEC Grade 1–3 literacy lists, and Lebanese/Arabic sight-word lists (MSA).
- Scoring: each word's frequency is divided by the total frequency of its source list, giving a score of 0 if the word is absent. Very small lists can distort these normalised values.
- The 100 most frequent words in the Doha list include 38 nouns, while the Prenke Romich 100 core words include none. The SEC and sight-word lists have 30 and 38 nouns in their top 100.
- Cited reference figures put nouns at 7% of the top 100 English words and 20% of the top 300. For MSA the shares are 26% and 45% (Buckwalter & Parkinson frequency dictionary).

## Interesting ideas
- The comparison splits a candidate lexicon against reference lists into three outputs: unsupported entries, shared entries with per-source scores, and missing entries ranked as candidates.
- Automatic scores only rank options, and a human expert makes the final accept/reject decision; the tool is rerun whenever new lists arrive.
- Checking part-of-speech proportions in a domain vocabulary against a reference exposed a structural mismatch, namely the noun-heavy Arabic lists.
- The authors keep spoken/colloquial lists apart from literacy/MSA reading lists, because the two registers call for different vocabularies.
- Users expressed adverbial meaning by combining a preposition with a noun rather than using a single adverb.

## For Mova

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

- **I1** (tokens): Running this three-way diff of our lexicon against school-text and fable word lists would surface missing lemmas and suspicious entries in crate en.
- **I2** (learn): LLM-teacher or witness-parser agreement scores could likewise rank lexicon and repair-rule proposals, with each acceptance logged as an explainable decision.
- **I3** (learn): Comparing tag distributions of silver data with gold treebanks could serve as a cheap negative-control check for tagger drift.
- **I4** (tokens): Keeping separate frequency profiles for children's tales and for exam prose could inform tagger priors and unknown-word handling per register.
- **I5** (frames): Prepositional phrases and adverbs could be mapped to the same manner/time/place roles in crate prag, so that equivalent meanings yield identical frames.

*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 · 598 works · 191,225 citations
- [Carnegie Mellon University Qatar](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university-qatar/) — University, Doha, Qatar · 48 works · 2,773 citations
- [University of Southampton](https://md.research.elamur.ai/free/en/universities/university-of-southampton/) — University, Southampton, United Kingdom · 13 works · 2,608 citations

Also on the byline: [Hamad Medical Corporation](https://ror.org/02zwb6n98) · [Mada Assistive Technology Centre](https://ror.org/05w0gd052).

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## Cite

Cite as: E.A. Draffan, Mike Wald, Nawar Halabi, Ouadie Sabia, Wajdi Zaghouani, Amatullah Kadous, Amal Idris, Nadine Zeinoun, David Banes and Dana Lawand. 2015. Generating acceptable Arabic Core Vocabularies and Symbols for AAC users. Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies.

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

```bibtex
@inproceedings{draffan2015generating,
  title = {{Generating acceptable Arabic Core Vocabularies and Symbols for AAC users}},
  author = {E.A. Draffan, Mike Wald, Nawar Halabi, Ouadie Sabia, Wajdi Zaghouani, Amatullah Kadous, Amal Idris, Nadine Zeinoun, David Banes and Dana Lawand},
  booktitle = {Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies},
  year = {2015},
  doi = {10.18653/v1/w15-5116},
  url = {https://doi.org/10.18653/v1/w15-5116}
}
```

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- [next → Joint Arabic Segmentation and Part-Of-Speech Tagging](https://md.research.elamur.ai/free/en/papers/algahtani-2015-joint-arabic-segmentation-part-speech/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/4/)

## References

25 references, 1 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*