Robust Part-of-speech Tagging of Arabic Text

We present a new and improved part of speech tagger for Arabic text that incorporates a set of novel features and constraints. This framework is presented within the MADAMIRA software suite, a state-of-the-art toolkit for Arabic language processing. Starting from a linear SVM model with basic lexical features, we add a range of features derived from morphological analysis and clustering methods. We show that using these features significantly improves part-of-speech tagging accuracy, especially for unseen words, which results in better generalization across genres. The final model, embedded in a sequential tagging framework, achieved 97.15% accuracy on the main test set of newswire data, which is higher than the current MADAMIRA accuracy of 96.91% while being 30% faster.