---
title: "ALRT — paper by Hamed et al. (2022) — summary and ideas"
description: "The authors present ALRT (V1.0), a tool that produces Arabic LRTs automatically. 18 references."
authors: ["Osama Hamed", "Saeed Salah", "Abed Alhakim Freihat"]
year: 2022
venue: "Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022"
source: "https://aclanthology.org/2022.nsurl-1.7/"
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/hamed-2022-alrt-cutting-edge-tool-automatic/"
---
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# ALRT: Cutting Edge Tool for Automatic Generation of Arabic Lexical Recognition Tests

**Authors:** [Osama Hamed](https://md.research.elamur.ai/free/en/authors/osama-hamed/), Saeed Salah and [Abed Alhakim Freihat](https://md.research.elamur.ai/free/en/authors/abed-alhakim-freihat/) · **Year:** 2022 · **Venue:** [Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022](https://md.research.elamur.ai/free/en/venues/nsurl/) ([International Workshop on NLP Solutions for Under Resourced Languages co-located with ICNLSP - Short Papers 2022](https://md.research.elamur.ai/free/en/venues/nsurl/2022/)) · **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/2022.nsurl-1.7/) · [PDF](https://aclanthology.org/2022.nsurl-1.7.pdf)

## Summary
The paper tackles the lack of quick, automated vocabulary-size placement tests for Arabic. Lexical Recognition Tests (LRTs) ask a learner to decide whether each item is a real word or an invented one. For Arabic, the invented items (nonwords) have so far been crafted by hand or semi-automatically. The authors present ALRT (V1.0), a tool that produces Arabic LRTs automatically.

The tool works from a large collection of freely available Arabic text drawn from books, social media and news. This text was cleaned and reduced to one vocabulary item per line in UTF-8. To make a nonword, ALRT swaps letters in a real word for letters that look or sound alike, using orthographic similarity rules and a phonological similarity map. Character n-gram statistics limit which swaps are allowed, and any candidate that turns out to be a real corpus word is discarded. Word-frequency thresholds sort items into difficulty levels, so tests can be graded.

A subject-matter expert reviewed the generated nonwords as an extra quality check. The test engine is released on GitHub. Possible uses include placement testing for Arabic learners and support for spellcheckers.

## Key points
- ALRT V1.0 extends the authors' earlier framework (Salah et al., 2022) for generating Arabic LRTs automatically. The test engine is published at github.com/ohamed/ar-lrts.
- Nonword candidates come from two lists that are then merged:
  - one built with orthographic similarity rules;
  - one built with a phonological similarity map.
- A candidate is dropped if it occurs in the processed corpus, which ensures it is not a real word.
- Two frequency thresholds (Threshold1, Threshold2) select words in a target frequency band, allowing multi-level tests.
- For every word, the tool stores all character n-grams from bigrams up to length−1 grams. The frequencies of preceding and following characters then guide which similar letter is substituted.
- Planned work includes:
  - integrating the ALP lemmatizer;
  - adding tokenization, part-of-speech and diacritization recognition tests.

## Interesting ideas
- Build distractors by swapping in visually or phonetically confusable letters, then reject anything found in a corpus; the result is items that look like real words but are guaranteed not to be.
- Use character n-gram frequencies around the edited position to keep only substitutions that yield plausible letter sequences, rather than random edits.
- Use corpus frequency bands as a cheap, explainable control for item difficulty.
- Treat automatic generation as the main path and add a light expert check only to validate the output.
- Generated nonwords can serve both as test items and as reference material for proofreading.

## For Mova

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

- **I1** (learn): Such pseudo-words make clean negative controls, letting us check that the tagger and lemmatizer flag unknown forms instead of confidently assigning them a real lemma.
- **I2** (tokens): The same constraint could rank candidate corrections or guessed lemmas for out-of-vocabulary words, preferring forms whose letter sequences are common in English.
- **I3** (answer): School-course tests and word problems could be graded by the frequency band of their key vocabulary, separating reading-difficulty failures from reasoning failures.
- **I4** (learn): Silver data or generated test items could be sampled for brief human or LLM-teacher review, keeping the pipeline automatic while estimating its error rate.
- **I5** (tokens): A bank of near-miss nonwords could stress-test spelling normalization before parsing, checking that real typos are repaired and invented forms are not silently accepted.

*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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## Cite

Cite as: Osama Hamed, Saeed Salah and Abed Alhakim Freihat. 2022. ALRT: Cutting Edge Tool for Automatic Generation of Arabic Lexical Recognition Tests. Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022.

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

```bibtex
@inproceedings{hamed2022alrt,
  title = {{ALRT: Cutting Edge Tool for Automatic Generation of Arabic Lexical Recognition Tests}},
  author = {Osama Hamed, Saeed Salah and Abed Alhakim Freihat},
  booktitle = {Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022},
  year = {2022},
  url = {https://aclanthology.org/2022.nsurl-1.7/}
}
```

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## References

18 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ehsan Mohammed Abdelgadir and VSV Laxmi Ramana. 2017. A Handbook on “Introduction to Phonetics & Phonology”: For Arabic students. Notion Press. [find](https://scholar.google.com/scholar?q=Ehsan+Mohammed+Abdelgadir+and+VSV+Laxmi+Ramana%2E+2017%2E+A+Handbook+on+%E2%80%9CIntroduction+to+Phonetics+%26+Phonology%E2%80%9D%3A+For+Arabic+students%2E+Notion+Press%2E)
2. David A Balota, Melvin J Yap, Keith A Hutchison, Michael J Cortese, Brett Kessler, Bjorn Loftis, James H Neely, Douglas L Nelson, Greg B Simpson, and Rebecca Treiman. 2007. The english lexicon project. Behavior research methods, 39(3):445–459. [↗](https://doi.org/10.3758/bf03193014)
3. Wouter Duyck, Timothy Desmet, Lieven PC Verbeke, and Marc Brysbaert. 2004. Wordgen: A tool for word selection and nonword generation in dutch, english, german, and french. Behavior Research Methods, Instruments, & Computers, 36(3):488–499. [↗](https://doi.org/10.3758/bf03195595)
4. Heba Elfardy and Mona Diab. 2012. [Aida: Automatic identification and glossing of dialectal arabic](https://md.research.elamur.ai/free/en/papers/elfardy-2012-aida-automatic-identification-glossing-dialectal/). In Proceedings of the 16th eamt conference (project papers), pages 83–83.
5. Nick C Ellis. 2002. Frequency effects in language processing: A review with implications for theories of implicit and explicit language acquisition. Studies in second language acquisition, 24(2):143–188. [↗](https://doi.org/10.1017/s0272263102002024)
6. A. A. Freihat, M. Abbas, G. Bella, and F. Giunchiglia. 2018a. Towards an optimal solution to lemmatization in arabic. In Proceedins of the 4th International Conference on Arabic Computational Linguistics (ACLing 2018), pages 1–9. [find](https://scholar.google.com/scholar?q=A%2E+A%2E+Freihat%2C+M%2E+Abbas%2C+G%2E+Bella%2C+and+F%2E+Giunchiglia%2E+2018a%2E+Towards+an+optimal+solution+to+lemmatization+in+arabic%2E+In+Proceedins+of+the+4th+International+Conference+on+Arabic+Computational+Linguist)
7. A. A. Freihat, G. Bella, H. Mubarak, and F. Giunchiglia. 2018b. A single-model approach for arabic segmentation, pos tagging, and named entity recognition. In 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), pages 1–8. [find](https://scholar.google.com/scholar?q=A%2E+A%2E+Freihat%2C+G%2E+Bella%2C+H%2E+Mubarak%2C+and+F%2E+Giunchiglia%2E+2018b%2E+A+single%2Dmodel+approach+for+arabic+segmentation%2C+pos+tagging%2C+and+named+entity+recognition%2E+In+2018+2nd+International+Conference+on+Natu)
8. Hicham Gueddah and Abdallah Yousfi. 2013. The impact of arabic inter-character proximity and similarity on spell-checking. In 2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA), pages 1–4. IEEE. [find](https://scholar.google.com/scholar?q=Hicham+Gueddah+and+Abdallah+Yousfi%2E+2013%2E+The+impact+of+arabic+inter%2Dcharacter+proximity+and+similarity+on+spell%2Dchecking%2E+In+2013+8th+International+Conference+on+Intelligent+Systems%3A+Theories+and+App)
9. Osama Hamed and Torsten Zesch. 2015. Generating nonwords for vocabulary proficiency testing. In Proceeding of the 7th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pages 473– 477. *Figure 3: The proposed block diagram of Arabic Lexical Recognition Test (LRT)* Osama Hamed and Torsten Zesch. 2017. The role of diacritics in designing lexical recognition tests for arabic. Procedia Computer Science, 117:119–128. [↗](https://doi.org/10.17185/duepublico/72102)
10. Osama Hamed and Torsten Zesch. 2018. The role of diacritics in adapting the difficulty of arabic lexical recognition tests. NLP for Computer Assisted Language Learning (NLP4CALL 2018), 23. [find](https://scholar.google.com/scholar?q=Osama+Hamed+and+Torsten+Zesch%2E+2018%2E+The+role+of+diacritics+in+adapting+the+difficulty+of+arabic+lexical+recognition+tests%2E+NLP+for+Computer+Assisted+Language+Learning+%28NLP4CALL+2018%29%2C+23%2E)
11. Osama Amin Hamed. 2019. Automatic generation of lexical recognition tests using natural language processing. Ph.D. thesis, Dissertation, Duisburg, Essen, Universität Duisburg-Essen, 2019. [↗](https://doi.org/10.17185/duepublico/70628)
12. Mohamed Osman Hegazi. 2016. An approach for arabic root generating and lexicon development. Int. J. Comp. Sci. Netw. Sec.(IJCSNS), 16(1):9. [find](https://scholar.google.com/scholar?q=Mohamed+Osman+Hegazi%2E+2016%2E+An+approach+for+arabic+root+generating+and+lexicon+development%2E+Int%2E+J%2E+Comp%2E+Sci%2E+Netw%2E+Sec%2E%28IJCSNS%29%2C+16%281%29%3A9%2E)
13. Ineke Huibregtse, Wilfried Admiraal, and Paul Meara. 2002. Scores on a yes-no vocabulary test: Correction for guessing and response style. Language testing, 19(3):227–245. [↗](https://doi.org/10.1191/0265532202lt229oa)
14. Emmanuel Keuleers and Marc Brysbaert. 2010. Wuggy: A multilingual pseudoword generator. Behavior research methods, 42(3):627–633. [↗](https://doi.org/10.3758/brm.42.3.627)
15. Kristin Lemhöfer and Mirjam Broersma. 2012. Introducing lextale: A quick and valid lexical test for advanced learners of english. Behavior research methods, 44(2):325–343. [↗](https://doi.org/10.3758/s13428-011-0146-0)
16. Kathleen Rastle, Jonathan Harrington, and Max Coltheart. 2002. 358,534 nonwords: The arc nonword database. The Quarterly Journal of Experimental Psychology Section A, 55(4):1339–1362. [↗](https://doi.org/10.1080/02724980244000099)
17. Robert Stephen Ricks. 2015. The development of frequency-based assessments of vocabulary breadth and depth for L2 Arabic. Georgetown University. [find](https://scholar.google.com/scholar?q=Robert+Stephen+Ricks%2E+2015%2E+The+development+of+frequency%2Dbased+assessments+of+vocabulary+breadth+and+depth+for+L2+Arabic%2E+Georgetown+University%2E)
18. Saeed Salah, Mohammad Nassar, Raid Zaghal, and Osama Hamed. 2022. Towards the automatic generation of arabic lexical recognition tests using orthographic and phonological similarity maps. Journal of King Saud University-Computer and Information Sciences, 34(10):8429–8439. [↗](https://doi.org/10.1016/j.jksuci.2021.02.006)

*Updated 1 November 2026*