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# A Neural Architecture for Dialectal Arabic Segmentation

**Authors:** [Younes Samih](https://md.mova.elamur.ai/free/en/docs/papers/authors/younes-samih/); [Mohammed Attia](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohammed-attia/); [Mohamed Eldesouki](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohamed-eldesouki/); [Ahmed Abdelali](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/); [Hamdy Mubarak](https://md.mova.elamur.ai/free/en/docs/papers/authors/hamdy-mubarak/); Laura Kallmeyer; [Kareem Darwish](https://md.mova.elamur.ai/free/en/docs/papers/authors/kareem-darwish/) · **Year:** 2017 · **Venue:** Proceedings of the Third Arabic Natural Language Processing Workshop **Link:** [aclanthology.org/W17-1306](https://aclanthology.org/W17-1306/) · library `aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**58** citations](https://openalex.org/works?filter=cites:W2614862557) openalex · 2026-10-18[**12** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation/) of the 140673 papers whose bibliographies we read[Proceedings of the Third Arabic Natural Language Processing Workshop](https://aclanthology.org/volumes/W17-13/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W17-1306/)
- [PDF](https://aclanthology.org/W17-1306.pdf)
- [DOI 10.18653/v1/w17-1306](https://doi.org/10.18653/v1/w17-1306)
- [OpenAlex](https://openalex.org/W2614862557)

**Authors (7)**

1. [Younes Samih](https://openalex.org/A5086381528) [ORCID](https://orcid.org/0000-0002-0485-7920) · h 19 · 996 citations · 70 works
2. [Mohammed Attia](https://openalex.org/A5075586667) h 18 · 1332 citations · 43 works
3. [Mohamed Eldesouki](https://openalex.org/A5033221342) [ORCID](https://orcid.org/0009-0009-6662-5883) · h 6 · 198 citations · 88 works
4. [Ahmed Abdelali](https://openalex.org/A5047182137) [ORCID](https://orcid.org/0000-0002-4160-8181) · h 25 · 2329 citations · 137 works
5. [Hamdy Mubarak](https://openalex.org/A5102924560) [ORCID](https://orcid.org/0000-0002-9051-6240) · h 30 · 3926 citations · 113 works
6. [Laura Kallmeyer](https://openalex.org/A5004268590) [ORCID](https://orcid.org/0000-0001-9691-5990) · h 18 · 1460 citations · 219 works
7. [Kareem Darwish](https://openalex.org/A5111452036) h 38 · 4980 citations · 151 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Egyptian Arabic in tweets has no standard spelling and few annotated resources, and the clitics attached to its verbs are missed by MSA tools. The authors segment words character by character with a bidirectional LSTM and a CRF output layer, labelling each character as beginning, middle, end, single or word boundary. The model is trained on only 200 sentences of an annotated set of 350 Egyptian tweets (75 for development, 75 for test), with pre-trained character embeddings and no lexicon. On the 1,449-word test set the BiLSTM-CRF reaches 92.65% accuracy, against 92.47% for MADAMIRA and 88.34% for Farasa; the BiLSTM without the CRF reaches 86.27%. The 0.18-point gap to MADAMIRA is not significant, and 40% of the test words are unseen in training.

## Ideas

- I1. Replace the hand-written segmentation tables of `tools/hbo` with a CRF over boundary labels (B, M, E, S, word boundary), using character features and no LSTM. The CRF enforces legal label sequences, which the tables try to do by hand. Compare its boundary errors with the table segmenter on the same MACULA tokens.
- I2. Score boundary labels per character, not only word accuracy. The paper's test set shows that word accuracy hides where the errors are; the clitic classes (the conjunction w, the negation prefix m, the negative suffix $) are the cases to check.
- I3. A learning curve on the size of the gold set. The paper gets its result with 200 sentences. Measure how much gold the segmenter needs for `languages/jpa/` and `languages/tmr/` before it beats a table-only baseline.

## For Mova

I1 and I2 go into `tools/hbo`, the segmenter for unpointed text. I3 is a test plan for the Aramaic varieties `languages/jpa/` and `languages/tmr/`. The BiLSTM itself is not reused; the label set and the CRF idea are.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/w17-1306](https://doi.org/10.18653/v1/w17-1306)

```
@inproceedings{samih2017neural,
  title = {{A Neural Architecture for Dialectal Arabic Segmentation}},
  author = {Samih, Younes and Attia, Mohammed and Eldesouki, Mohamed and Abdelali, Ahmed and Mubarak, Hamdy and Kallmeyer, Laura and Darwish, Kareem},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  year = {2017},
  doi = {10.18653/v1/w17-1306},
  url = {https://doi.org/10.18653/v1/w17-1306}
}
```

- [← previous Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-sajjad-challenging-language-dependent-segmentation-arabic/)
- [next → Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-zalmout-don-t-throw-those-morphological/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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