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Open Access Publications from the University of California

Part of speech tagging of Levantine

  • Author(s): Monirabbassi, Azadeh
  • et al.

The goal for this project is to explore strategies in adapting a Part of Speech (POS) tagger that was trained on Modern Standard Arabic sentences for tagging Levantine sentences, a dialect of Modern Standard Arabic, leveraged by methods of morphological analysis. I propose a tagging model that supports an explicit representation of the root -template patterns of Arabic. I will analyze the functionality and performance of the algorithms, and will compare the results. In leveraging the MSA POS tagger for tagging Levantine data, I achieved a peak accuracy of 73.28% which is 6% higher than the baseline for a standard Hidden Markov Model based tagger

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