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# Statistical Parsing by Machine Learning from a Classical Arabic Treebank

**Authors:** [Kais Dukes](https://md.mova.elamur.ai/free/en/docs/papers/authors/kais-dukes/) · **Year:** 2015 (PhD thesis, University of Leeds, 2013) · **Venue:** arXiv:1510.07193 **Link:** [arxiv.org/abs/1510.07193](https://arxiv.org/abs/1510.07193) · library `langstream-2015-dukes-statistical-parsing-machine-learning-classical` · **Licence:** [arXiv — non-exclusive license to distribute](https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html) — summary only

[**25** citations](https://openalex.org/works?filter=cites:W2236569115) openalex · 2026-10-18[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/langstream-2015-dukes-statistical-parsing-machine-learning-classical/) of the 140673 papers whose bibliographies we read[arXiv:1510.07193](https://openalex.org/S4306401585)[licence](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

- [Publisher's page](https://arxiv.org/abs/1510.07193)
- [PDF](https://arxiv.org/pdf/1510.07193)
- [arXiv 1510.07193](https://arxiv.org/abs/1510.07193)
- [OpenAlex](https://openalex.org/W2236569115)

**Authors (1)**

1. [Kais Dukes](https://openalex.org/A5021627574) h 8 · 410 citations · 21 works

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

## What it is about

A thesis built on seven papers about annotating and parsing Classical Arabic, with the Quran as the corpus. It defines three layers: an orthographic one for the Uthmani script (a character-plus-diacritic representation in place of raw Unicode, with location notation chapter:verse:word:segment), a lexeme-plus-feature morphological layer with segment-level POS, and a hybrid syntactic layer close to traditional Arabic grammar (iʿrāb): dependencies between words, but also between a word and a phrase, and explicit nodes for ellipsis (ḥadhf) restored by reconstruction (taqdīr) — dropped subject pronouns implied by the verb's features, syntactically required words, and semantically restored words. Annotation was done online by volunteers under expert supervision; the morphological layer reached 98.7% accuracy against reference grammars. The parser HSP builds hybrid dependency-constituency trees with ellipsis and reaches F1 89.03% on an extended labelled attachment score, against 87.47% for a pure dependency parser with graph post-processing. The author notes that annotators trained in traditional grammar were confused by pure dependency analyses of coordination and PP attachment, and that variant readings (e.g. one verse whose legal meaning depends on the head of a conjunct) were annotated with a single reading only. The card is written from the abstract, chapter 1, §6.2.4 on ellipsis and the concluding chapter.

## Ideas

- I1. Explicit elided nodes for dropped pronouns: a finite verb with no overt subject gets an empty `nsubj` node carrying the person/gender/number of the verb, as traditional grammar restores it. Biblical Hebrew and Aramaic are pro-drop in the same way; the frame reader can fill the agent slot from this node and say why.
- I2. A phrase-level attachment (word → phrase) for the cases traditional grammar treats that way, kept as a secondary layer on top of the UD tree rather than replacing it; measure with an extended attachment score that also counts the extra nodes.
- I3. Character-plus-diacritic encoding as the internal form of pointed text, with Unicode only at the edges: each letter is a struct with its marks, which makes search and rule matching on vowels, dagesh and accents deterministic and cheap. This is the form `tools/hbo` and `tools/ocrfix` could share for pointed Hebrew.
- I4. Variant readings as a recorded fork: where the grammar of a verse depends on the reading (or the head of a conjunct), store both analyses with their source instead of choosing silently. The ketiv/qere sites and disputed attachments in the Hebrew Bible are the first cases.
- I5. A location notation (book:chapter:verse:word:segment) as the key for every annotation, so that morphology, syntax and pragmatics on the same segment can be joined; MACULA ids already do this for Hebrew.

## For Mova

I1 goes into [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/) (pro-drop) and the frame reader in `prag`/`world`; I2 is an option for `tools/macula`; I3 into [1. Script and pointing](https://md.mova.elamur.ai/free/en/docs/languages/hbo/01-script/) and the shared text type of `tools/hbo` and `tools/ocrfix`; I4 into `tools/macula` (ketiv/qere) and [13. Biblical Hebrew in Mova: the tools](https://md.mova.elamur.ai/free/en/docs/languages/hbo/13-mova-tools/); I5 matches the MACULA ids used in `tools/macula`.

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 1510.07193](https://arxiv.org/abs/1510.07193)

```
@misc{dukes2015statistical,
  title = {{Statistical Parsing by Machine Learning from a Classical Arabic Treebank}},
  author = {Dukes, Kais},
  year = {2015},
  eprint = {1510.07193},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1510.07193}
}
```

- [← previous Probabilistic Modelling of Morphologically Rich Languages](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2015-botha-probabilistic-modelling-morphologically-rich-languages/)
- [next → Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2017-eldesouki-arabic-multi-dialect-segmentation-bi/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

221 references, 19 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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