The Effect of Automatic Tokenization, Vocalization, Stemming, and {POS} Tagging on {A}rabic Dependency Parsing

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.