A Pipeline Approach to Supervised Error Correction for the QALB-2014 Shared Task

This paper describes our submission to the ANLP-2014 shared task on auto-matic Arabic error correction. We present a pipeline approach integrating an er-ror detection model, a combination of character- and word-level translation mod-els, a reranking model and a punctuation insertion model. We achieve an F1 score of 62.8 % on the development set of the QALB corpus, and 58.6 % on the official test set.