Supervised learning model for parsing Arabic language

TL;DR

This paper presents a supervised machine learning approach using SVMs for parsing Arabic, addressing resource scarcity and demonstrating promising results on the Penn Arabic Treebank.

Contribution

It introduces a novel SVM-based parsing method tailored for Arabic language, leveraging existing annotated corpora for improved syntactic analysis.

Findings

Encouraging parsing accuracy results

Effective SVM-based label selection

Validated on Penn Arabic Treebank

Abstract

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.