Part of Speech tagging of Levantine

by Azadeh Monirabbassi

Type Thesis
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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 tag…

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Summary

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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Edition Statements

  • Publication Date:2008

Thesis Information

  • Degree:M.S.
  • University:University of California, San Diego
  • Advisor/Supervisor:G. W. L. Cottrell, Roger
  • Pages:50

Publication Statements

  • Language:eng
  • Publication Date:2008

Edition Identifiers

  • proquest:304659100

Subjects

Applied Sciences Arabic artificial intelligence computational linguistics Computer Science eng linguistics Machine learning Natural Language Processing PDF

Writers

Azadeh Monirabbassi G. W. L. Cottrell, Roger

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