Supervised learning model for parsing Arabic language
This addresses the problem of parsing Arabic, which is challenging due to language specificities and scarce digital resources, but the approach is incremental as it applies an existing method (SVMs) to this domain.
The authors tackled Arabic language parsing by developing a supervised machine learning method using SVMs to assign syntactic labels, achieving very encouraging results as evaluated on the Penn Arabic Treebank with cross-validation.
Parsing the Arabic language is a difficult task given the specificities of this language and given the scarcity of digital resources (grammars and annotated corpora). In this paper, we suggest a method for Arabic parsing based on supervised machine learning. We used the SVMs algorithm to select the syntactic labels of the sentence. Furthermore, we evaluated our parser following the cross validation method by using the Penn Arabic Treebank. The obtained results are very encouraging.