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Dependency Parsing of Modern Standard Arabic with Lexical and Inflectional Features

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

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. I3 is a training run of gparse, and I4 is a comparison of tag sets in tools/hbo, reported with The verb for the verb features.

Topics: Dependency parsing · 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

@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}
}

References

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