TECHLIMED$@$QALB-Shared Task 2015: a hybrid Arabic Error Correction System

Abstract

This paper reports on the participation of Techlimed in the Second Shared Task on Automatic Arabic Error Correction organized by the Arabic Natural Language Processing Workshop. This year's competition includes two tracks, and, in addition to errors produced by native speakers (L1), also includes correction of texts written by learners of Arabic as a foreign language (L2). Techlimed participated in the L1 track. For our participation in the L1 evaluation task, we developed two systems. The first one is based on the spellchecker Hunspell with specific dictionaries. The second one is a hybrid system based on rules, morphology analysis and statistical machine translation. Our results on the test set show that the hybrid system outperforms the lexicon driven approach with a precision of 71.2%, a recall of 64.94% and an F-measure of 67.93%.

The Second QALB Shared Task on Automatic Text Correction for Arabic

“…In addition to this difference, there are almost twice as many deletions and five time more moves in the L2 data, which could be due to grammatical errors that are not typical for native speakers. (Nawar, 2015) Cairo University (Egypt) GWU (Attia et al, 2015) George Washington University (USA) QCMUQ Carnegie Mellon University in Qatar (Qatar) and Qatar Computing Research Institute (Qatar) QCRI (Mubarak et al, 2015) Qatar Computing Research Institute (Qatar) SAHSOH (Zaghouani et al, 2015b) Bouira University (Algeria) and Carnegie Mellon University in Qatar (Qatar) TECH (Mostefa et al, 2015) Techlimed.com (France) UMMU (Bougares and Bouamor, 2015) Laboratoire d'Informatique de l'Université du Maine (France) and Carnegie Mellon University in Qatar (Qatar)…”