--- title: "A New Error Annotation for Dyslexic texts in Arabic — Alamri 2017" description: "Alamri and Teahan build such a taxonomy, DECA (Dyslexic error classification for Arabic texts). 13 references, cited by 1 paper here." authors: ["Maha Alamri", "William J Teahan"] year: 2017 venue: "Proceedings of the Third Arabic Natural Language Processing Workshop" source: "https://aclanthology.org/W17-1309/" doi: "10.18653/v1/w17-1309" 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/alamri-2017-new-error-annotation-dyslexic-texts/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alamri-2017-new-error-annotation-dyslexic-texts/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2017](https://md.research.elamur.ai/free/en/papers/2017/page/5/) / A New Error Annotation for Dyslexic texts in Arabic # A New Error Annotation for Dyslexic texts in Arabic **Authors:** Maha Alamri and William J Teahan · **Year:** 2017 · **Venue:** [Proceedings of the Third Arabic Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2017](https://md.research.elamur.ai/free/en/venues/wanlp/2017/)) · **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/W17-1309/) · [PDF](https://aclanthology.org/W17-1309.pdf) · [DOI 10.18653/v1/w17-1309](https://doi.org/10.18653/v1/w17-1309) · [OpenAlex](https://openalex.org/W2741814453) **Citations:** 10 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read ## Summary Arabic has had no standard scheme for classifying the spelling errors of dyslexic writers. Earlier work mostly listed typical mistakes without organising them into a taxonomy. Alamri and Teahan build such a taxonomy, DECA (Dyslexic error classification for Arabic texts). They drew on prior error-analysis studies and on real texts by dyslexic children. DECA groups errors into nine categories (Hamza, Almadd, Confusion, Diacritics, Form, and others) with 37 error types, plus "Other" fallbacks. The scheme was refined in two stages: - **Pilot annotation:** a 5,000-word sample was annotated, which surfaced two missing types. - **Expert check:** two special-education teachers reviewed the revised scheme. The authors then fully annotated BDAC, a 27,136-word corpus of texts by diagnosed dyslexic Saudi students, containing about 8,000 errors. They used a custom Java tool that stores annotations as XML. In the analysis, the "Common errors" category is the largest, and Alif Madd is the single most frequent error type. The work gives Arabic its first dyslexia error resource, useful for pedagogy, research and future spell-checking aids. ## Key points - DECA Version 2 has 9 categories and 37 error types. Version 1 had 35 types; "Repeated letters" and letter-shape-by-position (beginning/middle/end) were added after the pilot. - BDAC contains 27,136 words and roughly 8,000 errors. The writers are boys and girls aged 8–12 (a few aged 13), and the sources are Saudi primary schools, online forms and parents. - Error counts by category: - Common errors: 2,717 - Hamza: 1,621 - Confusion: 1,553 - Differences and Form: the least frequent categories. - Alif Madd errors make up 13.43% of all errors. The most frequent single erroneous word is a misspelling of "on", with 64 occurrences. - Tags use two letters, category then type (e.g. HA), following Alfaifi et al. (2013). A word with several errors gets joined tags such as HA_MA. Each XML record stores the token number, error form, correct form and tag. ## Interesting ideas - Before committing to full annotation, the authors annotated a small sample with the draft scheme and turned the leftover cases into new types; this exposes coverage gaps cheaply. - Tags are built compositionally (category letter plus type letter), with an explicit "Other" slot inside every category, so unknown cases stay countable instead of being forced into a wrong label. - A token carrying several independent errors gets several joined tags rather than one blended label, which keeps the statistics per error type clean. - Every record pairs the erroneous form with its corrected form and token position, so the corpus doubles as training data for a corrector. - Annotators were domain teachers, and they were asked about both tag correctness and how easy it was to choose a tag; ease of choosing is a useful signal of ambiguous categories. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): Run a pilot of any new silver-label or repair-rule category set on a few hundred sentences, logging cases that fit no label, before scaling. - **I2** (tokens): Typo and normalisation categories in the en crate could use hierarchical codes with per-group "other" buckets, keeping unexplained cases visible in evaluation. - **I3** (tokens): When the tokenizer repairs a misspelled word, it could record each edit as a separate reason, making the correction fully explainable. - **I4** (learn): Noisy-input test sets for the parser could store original/corrected pairs with positions, so robustness can be measured against a clean-text control. - **I5** (frames): When LLM teachers or humans assign semantic roles or speech acts, also collecting a difficulty rating could flag frame labels that need clearer definitions. *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 Also on the byline: [Bangor University](https://ror.org/006jb1a24). 📚 Shelves (6): [🖥️ 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 · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,552 works · [💭 Developmental and Educational Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/developmental-and-educational-psychology/) · 499 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works ## Cite Cite as: Maha Alamri and William J Teahan. 2017. A New Error Annotation for Dyslexic texts in Arabic. Proceedings of the Third Arabic Natural Language Processing Workshop. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alamri2017new, title = {{A New Error Annotation for Dyslexic texts in Arabic}}, author = {Maha Alamri and William J Teahan}, booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop}, year = {2017}, doi = {10.18653/v1/w17-1309}, url = {https://doi.org/10.18653/v1/w17-1309} } ``` - [← previous UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech](https://md.research.elamur.ai/free/en/papers/bulut-2017-utd-crss-submission-mgb-3/) - [next → Automatic Negation and Speculation Detection in Veterinary Clinical Text](https://md.research.elamur.ai/free/en/papers/cheng-2017-automatic-negation-speculation-detection-veterinary/) - [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/5/) ## References 13 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Maha M. Alamri and William J. Teahan. 2013. Investigating dyslexic Arabic text. Master’s thesis, School of Computer Science, Bangor. [find](https://scholar.google.com/scholar?q=Maha+M%2E+Alamri+and+William+J%2E+Teahan%2E+2013%2E+Investigating+dyslexic+Arabic+text%2E+Master%E2%80%99s+thesis%2C+School+of+Computer+Science%2C+Bangor%2E) 2. Abdullah Alfaifi, Eric Atwell, and Ghazi Abuhakema. 2013. Error annotation of the arabic learner corpus. In Language Processing and Knowledge in the Web, pages 14–22. Springer. [find](https://scholar.google.com/scholar?q=Abdullah+Alfaifi%2C+Eric+Atwell%2C+and+Ghazi+Abuhakema%2E+2013%2E+Error+annotation+of+the+arabic+learner+corpus%2E+In+Language+Processing+and+Knowledge+in+the+Web%2C+pages+14%E2%80%9322%2E+Springer%2E) 3. Mohammed A. M. Ali. 2011. Learning difficulties between skills and disorders. Dar Safa Publishing -Distribution, Amman. 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[find](https://scholar.google.com/scholar?q=James+J%2E+Gallagher+and+Samuel+A%2E+Kirk%2E+1989%2E+Educating+exceptional+children%2E+Boston%3A+Houghton+Mifflin+Company%2E) 7. Esfandiari B. Ghazaleh. 2011. A study of developmental dyslexia in middle school foreign language learners in iran. Argumentum, 7:159–169. [find](https://scholar.google.com/scholar?q=Esfandiari+B%2E+Ghazaleh%2E+2011%2E+A+study+of+developmental+dyslexia+in+middle+school+foreign+language+learners+in+iran%2E+Argumentum%2C+7%3A159%E2%80%93169%2E) 8. Sylviane Granger. 2003. Error-tagged learner corpora and call: A promising synergy. CALICO journal, pages 465–480. [find](https://scholar.google.com/scholar?q=Sylviane+Granger%2E+2003%2E+Error%2Dtagged+learner+corpora+and+call%3A+A+promising+synergy%2E+CALICO+journal%2C+pages+465%E2%80%93480%2E) 9. Diane Nicholls. 2003. The cambridge learner corpus: Error coding and analysis for lexicography and elt. In Proceedings of the Corpus Linguistics 2003 conference, volume 16, pages 572–581. [find](https://scholar.google.com/scholar?q=Diane+Nicholls%2E+2003%2E+The+cambridge+learner+corpus%3A+Error+coding+and+analysis+for+lexicography+and+elt%2E+In+Proceedings+of+the+Corpus+Linguistics+2003+conference%2C+volume+16%2C+pages+572%E2%80%93581%2E) 10. Jennifer Pedler. 2007. Computer correction of real-word spelling errors in dyslexic text. Ph.D. thesis, Birkbeck, University of London. [find](https://scholar.google.com/scholar?q=Jennifer+Pedler%2E+2007%2E+Computer+correction+of+real%2Dword+spelling+errors+in+dyslexic+text%2E+Ph%2ED%2E+thesis%2C+Birkbeck%2C+University+of+London%2E) 11. James Pustejovsky and Amber Stubbs. 2012. Natural language annotation for machine learning. O’Reilly Media, Inc. [find](https://scholar.google.com/scholar?q=James+Pustejovsky+and+Amber+Stubbs%2E+2012%2E+Natural+language+annotation+for+machine+learning%2E+O%E2%80%99Reilly+Media%2C+Inc%2E) 12. Maria Rauschenberger, Luz Rello, Silke Fchsel, and Jrg Thomaschewski. 2016. A language resource of German errors written by children with dyslexia. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, may. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Maria+Rauschenberger%2C+Luz+Rello%2C+Silke+Fchsel%2C+and+Jrg+Thomaschewski%2E+2016%2E+A+language+resource+of+German+errors+written+by+children+with+dyslexia%2E+In+Proceedings+of+the+Tenth+International+Conference) 13. Luz Rello. 2014. A Text Accessibility Model for People with Dyslexia. Ph.D. thesis, Department of Information and Communication Technologies, University Pompeu Fabra. [find](https://scholar.google.com/scholar?q=Luz+Rello%2E+2014%2E+A+Text+Accessibility+Model+for+People+with+Dyslexia%2E+Ph%2ED%2E+thesis%2C+Department+of+Information+and+Communication+Technologies%2C+University+Pompeu+Fabra%2E) *Updated 31 October 2026*