CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Multilingual dependency analysis with a two-stage discriminative parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Developing an Arabic treebank: methods, guidelines, procedures, and tools
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Improving Arabic dependency parsing with lexical and inflectional morphological features
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
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We use an automatic pipeline of word tokenization, stemming, POS tagging, and vocalization to perform real-world Arabic dependency parsing. In spite of the high accuracy on the modules, the very few errors in tokenization, which reaches an accuracy of 99.34%, lead to a drop of more than 10% in parsing, indicating that no high quality dependency parsing of Arabic, and possibly other morphologically rich languages, can be reached without (semi-)perfect tokenization. The other module components, stemming, vocalization, and part of speech tagging, do not have the same profound effect on the dependency parsing process.