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# Dependency Parsing of Modern Standard Arabic with Lexical and Inflectional Features

**Authors:** [Yuval Marton](https://md.mova.elamur.ai/free/en/docs/papers/authors/yuval-marton/); [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/); [Owen Rambow](https://md.mova.elamur.ai/free/en/docs/papers/authors/owen-rambow/) · **Year:** 2013 · **Venue:** Computational Linguistics **Link:** [aclanthology.org/J13-1008](https://aclanthology.org/J13-1008/) · library `cl-2013-marton-dependency-parsing-modern-standard-arabic` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**82** citations](https://openalex.org/works?filter=cites:W2157223933) openalex · 2026-10-18[**27** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cl-2013-marton-dependency-parsing-modern-standard-arabic/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J13-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J13-1008/)
- [PDF](https://aclanthology.org/J13-1008.pdf)
- [DOI 10.1162/coli_a_00138](https://doi.org/10.1162/coli_a_00138)
- [OpenAlex](https://openalex.org/W2157223933)

**Authors (3)**

1. [Yuval Marton](https://openalex.org/A5048736402) h 12 · 901 citations · 44 works
2. [Nizar Habash](https://openalex.org/A5084517393) [ORCID](https://orcid.org/0000-0002-1831-3457) · h 57 · 12233 citations · 359 works
3. [Owen Rambow](https://openalex.org/A5021314411) [ORCID](https://orcid.org/0000-0003-2054-039X) · h 46 · 9290 citations · 420 works

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

## What it is about

The paper asks which morphological features help dependency parsing of MSA, using CATiB trees and two transition-based parsers (MaltParser and Easy-First). Each feature set is tested under gold tags and under predicted tags. Finer tag sets help with gold tags but hurt when the tags are predicted; the coarse six-tag CATiB set is predicted most accurately and wins in that condition. Definiteness, person, number, gender and the undiacritized lemma help with predicted input. Functional gender and number (broken plurals, which are over half of the plurals in their training data) and rationality beat the form-based values. Case helps with gold tags but is the worst feature when predicted, because it is rarely written. Training on gold plus predicted features (g+p) improves parsing with predicted input, and the findings hold for both parsers.

## Ideas

- I1. Undiacritized lemma as the lexical feature in unpointed mode. For `gparse` input from `tools/hbo`, use the consonantal lemma rather than the pointed one, and test both in the predicted condition. The paper's best lexical feature has half the error rate of the diacritized lemma.
- I2. Functional number and gender for Hebrew nouns with irregular plurals or with a suffix that does not match the form. Keep a table in [The Noun, the Adjective and the Numeral](https://md.mova.elamur.ai/free/en/docs/languages/hbo/04-noun/) and let adjectives agree with the functional values. Test: adjective-noun agreement on `treebanks/hbo/ptnk`.
- I3. Train on gold and predicted tags together. The `gparse` training set adds the `tools/hbo` predictions for the same sentences. Compare it with gold-only training under predicted input; run twice to show the result is deterministic.
- I4. Choose the tag set by measured accuracy and relevance together. Do not use case vowels, which are absent from unpointed text, as a feature in predicted mode. Compare a coarse and a fine tag set on predicted input in `tools/hbo`.

## For Mova

I1 goes into `tools/hbo` (unpointed mode) and the `gparse` features. I2 is a table in [The Noun, the Adjective and the Numeral](https://md.mova.elamur.ai/free/en/docs/languages/hbo/04-noun/). I3 is a training run of `gparse`, and I4 is a comparison of tag sets in `tools/hbo`, reported with [The verb](https://md.mova.elamur.ai/free/en/docs/languages/hbo/06-verb/) for the verb features.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/coli_a_00138](https://doi.org/10.1162/coli_a_00138)

```
@article{marton2013dependency,
  title = {{Dependency Parsing of Modern Standard Arabic with Lexical and Inflectional Features}},
  author = {Marton, Yuval and Habash, Nizar and Rambow, Owen},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00138},
  url = {https://doi.org/10.1162/coli_a_00138}
}
```

- [← previous Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank](https://md.mova.elamur.ai/free/en/docs/papers/bank-2010-dukes-syntactic-annotation-guidelines-quranic-arabic/)
- [next → How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study](https://md.mova.elamur.ai/free/en/docs/papers/cl-2022-esmail-how-much-does-lookahead-matter/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

38 references, 10 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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