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# Farasa: A Fast and Furious Segmenter for Arabic

**Authors:** [Ahmed Abdelali](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmed-abdelali/); [Kareem Darwish](https://md.mova.elamur.ai/free/en/docs/papers/authors/kareem-darwish/); [Nadir Durrani](https://md.mova.elamur.ai/free/en/docs/papers/authors/nadir-durrani/); [Hamdy Mubarak](https://md.mova.elamur.ai/free/en/docs/papers/authors/hamdy-mubarak/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations **Link:** [aclanthology.org/N16-3003](https://aclanthology.org/N16-3003/) · library `aclbulk-2016-abdelali-farasa-fast-furious-segmenter-arabic` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**371** citations](https://openalex.org/works?filter=cites:W2471147443) openalex-snapshot · 2026-10-14[**135** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2016-abdelali-farasa-fast-furious-segmenter-arabic/) of the 140673 papers whose bibliographies we read[Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations](https://aclanthology.org/volumes/N16-3/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/N16-3003/)
- [PDF](https://aclanthology.org/N16-3003.pdf)
- [DOI 10.18653/v1/n16-3003](https://doi.org/10.18653/v1/n16-3003)
- [OpenAlex](https://openalex.org/W2471147443)

**Authors (4)**

1. [Ahmed Abdelali](https://openalex.org/A5047182137) [ORCID](https://orcid.org/0000-0002-4160-8181) · [Semantic Scholar](https://www.semanticscholar.org/author/1683403) · h 24 · 2180 citations · 132 works
2. [Kareem Darwish](https://openalex.org/A5111452036) [Semantic Scholar](https://www.semanticscholar.org/author/143758717) · h 38 · 4763 citations · 151 works
3. [Nadir Durrani](https://openalex.org/A5032689067) [ORCID](https://orcid.org/0000-0002-9378-4128) · [Semantic Scholar](https://www.semanticscholar.org/author/145938140) · h 31 · 3169 citations · 145 works
4. [Hamdy Mubarak](https://openalex.org/A5102924560) [ORCID](https://orcid.org/0000-0002-9051-6240) · [Semantic Scholar](https://www.semanticscholar.org/author/143779235) · h 30 · 3692 citations · 113 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Farasa splits an Arabic word into prefixes, stem and suffixes. Every character-level split that uses a legal prefix and suffix sequence is generated, and a linear SVM-rank model scores the candidates. The features are stem and affix likelihoods, lexicon membership (a list of valid stems, a gazetteer of names, a list of legal prefix-suffix combinations, a lexicon of stems generated from roots by stem templates) and the length of the stem. A second variant caches segmentations seen in training and applies the classifier only to unseen words. On a new 70-article WikiNews test set of about 18,000 words, Farasa matches MADAMIRA (98.76%) and the lookup variant reaches 98.94%. In machine translation it is about as good as MADAMIRA (BLEU 30.5 against 30.6 in the average row of its Arabic-to-English table) and about fifty times faster. In retrieval on TREC 2001/2002 it is the best of the three segmenters. The main errors are foreign names and very long words.

## Ideas

- I1. A legality table as a hard filter: list the prefixes and suffixes and the legal sequences between them (for example, a determiner cannot be followed by another prefix), and reject any split that breaks the table before any scoring. This is cheap, deterministic and easy to check against the `pre` and suffix tables that `tools/hbo` already has.
- I2. A stem-validity feature from roots: a candidate stem is accepted only if its root fits a known stem template, for Hebrew the root-and-pattern shapes of the binyanim (for example ktb in the template of katav). Build the stem list from the MACULA lemmas and test whether the feature removes wrong splits.
- I3. A held-out test built from fresh news, not from the Penn treebank alone, with the error types counted (foreign names, long words). Use the same design for `tools/hbo`, so that segmenter results are not inflated by a narrow training domain.

## For Mova

I1 and I2 go into the segmenter of `tools/hbo` (the `pre`/`lex` tables) and into [Pronouns, particles and prepositions](https://md.mova.elamur.ai/free/en/docs/languages/hbo/05-pronouns-particles/) for the legal sequences of prefixes (w, l, b, k, the article and the relative). I3 is a test set for `tools/hbo`, built from Hebrew text outside MACULA (for example quotations in commentaries), with the error types counted.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/n16-3003](https://doi.org/10.18653/v1/n16-3003)

```
@inproceedings{abdelali2016farasa,
  title = {{Farasa: A Fast and Furious Segmenter for Arabic}},
  author = {Abdelali, Ahmed and Darwish, Kareem and Durrani, Nadir and Mubarak, Hamdy},
  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
  year = {2016},
  doi = {10.18653/v1/n16-3003},
  url = {https://doi.org/10.18653/v1/n16-3003}
}
```

- [← previous Improving Arabic Diacritization through Syntactic Analysis](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2015-shahrour-improving-arabic-diacritization-through-syntactic/)
- [next → CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-shahrour-camelparser-system-arabic-syntactic-analysis/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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