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Improving Arabic Dependency Parsing with Form-based and Functional Morphological Features

What it is about

The authors ask which morphological features help a transition-based parser (MaltParser, eager algorithm, SVM classifiers) on Modern Standard Arabic. The data are the CATiB dependency trees converted from Penn Arabic Treebank part 3, with features predicted by MADA rather than taken from gold. Nine inflectional features and three lexical ones (undiacritized lemma, root, pattern) are added one at a time, greedily, and in combination. Definiteness, person/number/gender and the undiacritized lemma help most; adding all nine features together hurts, because low-accuracy predictions (case, 86.3%) dominate. Functional gender and number (the "broken plurals" case) and the rationality feature beat surface features: the best CORE 12 setup reaches 80.53% LAS against a 78.68% baseline, and the best setup overall, with the CATiB-EX tagset, reaches 80.83% LAS.

Ideas

For Mova

I1 belongs in tools/hbo (the feature gate before gparse). I2 is a test for the unpointed mode of tools/hbo, with macula dict as the lemma source. I3 is a report for gparse on treebanks/hbo/ptnk and macula compare. The paper's Arabic agreement facts are not transferred into The Noun, the Adjective and the Numeral without first checking them against the Hebrew treebank.

Topics: Dependency parsing · Treebanks

Cite

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

@inproceedings{marton2011improving,
  title = {{Improving Arabic Dependency Parsing with Form-based and Functional Morphological Features}},
  author = {Marton, Yuval and Habash, Nizar and Rambow, Owen},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1159/}
}

References

27 references, 8 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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  24. Joakim Nivre. 2009. Parsing Indian languages with MaltParser. In Proceedings of the ICON09 NLP Tools Contest: Indian Language Dependency Parsing, pages 12–18. find
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