---
title: "Part-of-Speech and Morphological Tagging of Algerian"
description: "Using the TAJA corpus, the authors build automatic POS and morphological taggers. 63 references."
authors: ["Ofra Tirosh-Becker", "Michal Kessler", "Oren Becker", "Yonatan Belinkov"]
year: 2022
venue: "Northern European Journal of Language Technology"
source: "https://nejlt.ep.liu.se/article/view/4315"
doi: "10.3384/nejlt.2000-1533.2022.4315"
licence: "CC BY 4.0"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/becker-2022-part-speech-morphological-tagging-algerian/"
---
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# Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic

**Authors:** Ofra Tirosh-Becker, Michal Kessler, Oren Becker and [Yonatan Belinkov](https://md.research.elamur.ai/free/en/authors/yonatan-belinkov/) · **Year:** 2022 · **Venue:** [Northern European Journal of Language Technology](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/) ([Northern European Journal of Language Technology 2022](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/2022/)) · **Publisher:** [Northern European Association for Language Technology](https://md.research.elamur.ai/free/en/publishers/northern-european-association-for-language-technology/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.3384/nejlt.2000-1533.2022.4315) · [PDF](https://nejlt.ep.liu.se/article/download/4315/3668) · [DOI 10.3384/nejlt.2000-1533.2022.4315](https://doi.org/10.3384/nejlt.2000-1533.2022.4315) · [OpenAlex](https://openalex.org/W4311428537)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Algerian Judeo-Arabic (AJA) is the Arabic of Algeria's Jewish communities, written in Hebrew script. It now has very few speakers, so linguists study it mainly through printed and handwritten texts from the 19th–20th centuries. Manual morphological annotation of these texts is slow and needs rare expertise, which keeps most studies limited to one or a few texts. Using the TAJA corpus, the authors build automatic POS and morphological taggers. They compare the off-the-shelf MarMoT tagger with several custom neural models. The strongest model is a character-level CNN (chaR-cnn) arranged in two tiers: it predicts POS first and then morphology conditioned on that POS. Their hybrid strategy, AJATag, lets a linguist fix the POS layer before the morphology tier runs. AJATag clearly beats MarMoT, with the largest gains on out-of-vocabulary words. A user study with expert linguists suggests the approach is practical for real annotation work and could let AJA research scale beyond single texts.

## Key points
- Morphological tagging takes about 80% of an expert's annotation time, so it is the main bottleneck the paper targets.
- AJATag (hierarchical chaR-cnn given corrected POS) reaches 89.0% on analysis1 and 92.7% on analysis2. MarMoT scores 82.3% and 85.6% on the same fields. Both systems exceed 96% on the enclitic field.
- On the "flexible" overall score, which credits each correct tag separately, AJATag gets 91.2% and MarMoT 89.0%. On words whose predicted tag set is a legal combination (over 80% of words), AJATag rises to 95.2%, while MarMoT stays roughly the same.
- Out-of-vocabulary words are 21% of the TAJA test set. On these, AJATag scores 74.91% (analysis1) and 78.42% (analysis2), against MarMoT's 55.82% and 59.95%. On additional tags the gap is 85.4% vs 75.0%. Even the fully automatic chaR-cnn edges out MarMoT on OOV POS (72.58% vs 71.35%).
- In the user study, a senior expert's POS corrections served as the reference. Both automatic taggers scored about 89% against it, close to the 91% achieved by a junior expert (a PhD candidate).

## Interesting ideas
- Splitting tagging into a POS tier and a morphology tier conditioned on it lets errors be fixed cheaply at the coarse level before they spread into the fine-grained features.
- Letting a human correct only the cheap first layer, and then rerunning the automatic second layer, gave the largest gains, especially on unseen words.
- The paper checks whether predicted tag bundles are legal for their POS and reports accuracy separately on the legal subset, which exposes a fixable class of errors.
- Reporting OOV accuracy separately reveals differences between systems that overall accuracy hides, and it better predicts how a tagger will behave on new text.
- Comparing the tagger with a junior human annotator, measured against a senior expert's reference, gives a meaningful ceiling and a test of practical usefulness.

## For Mova

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

- **I1** (tokens): The en tagger could predict UPOS first and then choose features conditioned on it, which would make each morphological decision traceable to a stated POS.
- **I2** (learn): An LLM teacher tier could fix only the POS layer in silver data, with Mova regenerating the features, so that expensive supervision is spent where it pays off most.
- **I3** (tokens): A table of allowed feature sets per UPOS could flag or repair impossible bundles, and the violation rate would serve as a cheap health metric for the tagger.
- **I4** (learn): Evaluations could always include an OOV slice, so that improvements that only memorize the training vocabulary show up quickly.
- **I5** (learn): Mova's parser and tagger scores could be set beside human inter-annotator agreement on a small sample, to judge when further tuning stops being worthwhile.

*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

- [Hebrew University of Jerusalem](https://md.research.elamur.ai/free/en/universities/hebrew-university-of-jerusalem/) — University, Jerusalem, Israel · 50 works · 9,147 citations
- [Technion – Israel Institute of Technology](https://md.research.elamur.ai/free/en/universities/technion-israel-institute-of-technology/) — University, Haifa, Israel · 28 works · 4,941 citations

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## Cite

Cite as: Ofra Tirosh-Becker, Michal Kessler, Oren Becker and Yonatan Belinkov. 2022. Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic. Northern European Journal of Language Technology.

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

```bibtex
@article{tiroshbecker2022part,
  title = {{Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic}},
  author = {Ofra Tirosh-Becker, Michal Kessler, Oren Becker and Yonatan Belinkov},
  journal = {Northern European Journal of Language Technology},
  year = {2022},
  doi = {10.3384/nejlt.2000-1533.2022.4315},
  url = {https://doi.org/10.3384/nejlt.2000-1533.2022.4315}
}
```

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*Updated 24 October 2026*