UMMU$@$QALB-2015 Shared Task: Character and Word level SMT pipeline for Automatic Error Correction of Arabic Text
نویسندگان
چکیده
In this paper we present the LIUM (Laboratoire d’Informatique de l’Universit du Maine) and CMU-Q (Carnegie Mellon University in Qatar) joint submission in the Arabic shared task on automatic spelling error correction. Our best system is a sequential combination of two statistical machine translation systems (SMT) trained on top of the MADAMIRA output. The first is a Character-based one, used to produce a first correction at the character level. Characters are then glued to form the input to the second system working at the Word level. This sequential combination achieves an F1 score of (69.42) that is better than the best F1 score reported on the 2014 test set (67.91). The UMMU best submission to the QALB-15 shared task is ranked first over 10 submission on the L2 test condition and second over 12 submission on the L1 testsset.
منابع مشابه
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