---
title: "AraSAS: The Open Source Arabic Semantic Tagger (2022)"
description: "The authors present AraSAS, which they call the first open-source Arabic semantic tagger. 91% accuracy. 33 references."
authors: ["Mahmoud El-Haj", "Elvis de Souza", "Nouran Khallaf", "Paul Rayson", "Nizar Habash"]
year: 2022
venue: "Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection"
source: "https://aclanthology.org/2022.osact-1.3/"
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/haj-2022-arasas-open-source-arabic-semantic/"
---
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# AraSAS: The Open Source Arabic Semantic Tagger

**Authors:** [Mahmoud El-Haj](https://md.research.elamur.ai/free/en/authors/mahmoud-el-haj/), Elvis de Souza, [Nouran Khallaf](https://md.research.elamur.ai/free/en/authors/nouran-khallaf/), [Paul Rayson](https://md.research.elamur.ai/free/en/authors/paul-rayson/) and [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) · **Year:** 2022 · **Venue:** [Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection](https://md.research.elamur.ai/free/en/venues/osact/) ([Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks 2022](https://md.research.elamur.ai/free/en/venues/osact/2022/)) · **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/2022.osact-1.3/) · [PDF](https://aclanthology.org/2022.osact-1.3.pdf)

## Summary
Arabic is hard to tag semantically. Its rich morphology, attachable clitics and usually omitted short vowels create heavy ambiguity before ordinary polysemy is even considered. Open tools for coarse word-sense annotation were also missing. The authors present AraSAS, which they call the first open-source Arabic semantic tagger. It is modelled on the English USAS system and uses the same hierarchical inventory of 21 major discourse fields and 232 finer semantic field tags.

The tool is written in Python 3 and works in stages:
- split the text into sentences,
- tokenise and morphologically disambiguate with CAMeL Tools, choosing a lemma and POS,
- look the result up in a substantially extended and edited semantic lexicon,
- tag numbers and punctuation with regular expressions.

On lexicon coverage, AraSAS matches roughly 96% of tokens in blogs and news, which is on par with the English tagger. A case study on the bilingual ZAEBUC student essays shows the main benefit. Because Arabic and English text share one semantic field scheme, the two languages, the same writers and male versus female authors can all be compared directly.

## Key points
- Earlier unreleased prototype (Mohamed et al., 2013) took English glosses of Buckwalter lemmas, matched them against the USAS English lexicon and merged all tags. This matched 71% of lemmas and produced 37,312 entries, but only 4% (the most frequent in the CCA corpus) were hand-corrected.
- Ambiguity scale: SAMA gives about 12 analyses per word on average. The form wlςyn yields 4 lemmas and 83 analyses in CALIMA-Star, and the lemma ςayn has around 50 out-of-context meanings.
- English USAS reaches about 91% accuracy using manually curated single-word and MWE lexicons totalling about 80,000 entries.
- Lexicon coverage of AraSAS is 96% on blogs and 96.8% on news. The ZAEBUC case study used 603 essays by bilingual UAE undergraduates.
- AraSAS is integrated with CAMeL Tools and released on GitHub (UCREL/AraSAS) and as a web tool.

## Interesting ideas
- The first lexicon was bootstrapped by routing foreign lemmas through their English glosses into an existing English lexicon, and human correction then focused on the highest-frequency lemmas to get the most coverage per hour of work.
- Each token gets an ordered list of candidate tags, with combined tags written using a slash, rather than one forced label, so later stages can still see the alternatives.
- Lemma and POS are disambiguated first, and only then is the semantic lexicon consulted, so lexical ambiguity is resolved after morphological ambiguity.
- Lexicon coverage on different genres is used as a cheap first evaluation, before any accuracy study.
- A shared coarse semantic field inventory lets texts in two languages be compared by field frequency and field diversity per text.

## For Mova

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

- **I1** (world): Mova could seed sense or class labels for rare words through WordNet glosses, then spend manual or LLM-teacher review only on the frequent entries.
- **I2** (frames): role and frame assignment could keep a ranked list of verb-class readings per predicate and let context choose, instead of committing early in the lexicon.
- **I3** (tokens): the en lemmatiser's output could act as the only key into semantic lexicons, keeping lookups explainable and preventing raw-surface matches.
- **I4** (learn): tracking the share of tokens covered by VerbNet, WordNet or other compiled knowledge on each test genre would show where unknown-word gaps are hurting downstream answers.
- **I5** (back): a coarse field profile of the source could be compared with that of generated or translated text, as an inexpensive check that meaning was not lost.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works

🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works

## Cite

Cite as: Mahmoud El-Haj, Elvis de Souza, Nouran Khallaf, Paul Rayson and Nizar Habash. 2022. AraSAS: The Open Source Arabic Semantic Tagger. Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection.

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

```bibtex
@inproceedings{elhaj2022arasas,
  title = {{AraSAS: The Open Source Arabic Semantic Tagger}},
  author = {Mahmoud El-Haj, Elvis {de Souza}, Nouran Khallaf, Paul Rayson and Nizar Habash},
  booktitle = {Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection},
  year = {2022},
  url = {https://aclanthology.org/2022.osact-1.3/}
}
```

- [← previous Arabic Sentiment Analysis by Pretrained Ensemble](https://md.research.elamur.ai/free/en/papers/qaddoumi-2022-arabic-sentiment-analysis-pretrained-ensemble/)
- [next → Barch: an English Dataset of Bar Chart Summaries](https://md.research.elamur.ai/free/en/papers/skrjanec-2022-barch-english-dataset-bar-chart/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/9/)

## References

33 references, 3 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 2 November 2026*