Imam Mohammad Ibn SaudIslamic UniversityCollege of Computing andInformation ScienceComputer sciences DepartmentPrepared by...
Arabic Tokenization andStemming
Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Introduction Arabic language. Tokenization. Stemming.
Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Arabic Language Characteristics• Writing the letter in ambiguous case cause orthography problems.• Encliticization of a wo...
Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
My Approach Sample of Arabic tokenized text: The Bigrams equation that used is:P(wi | sj) is probability of ith word giv...
Outline Introduction Arabic Characteristics. Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• ...
ResultsThe result of My Approach algorithm:• They used Bigrams on 45 files with size of 29092 tokens.• The final accuracy ...
Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Arabic Language Characteristics
Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Methodology Root-based. Light Stemmer. N-Gram. Hybrid Method.
Root-based Example of root-based stemmer
Light Stemmer Removed morphemes by Light stemmers
Light Stemmer Classification of Light8 stemmer
N-gram Statistical stemmer based on calculating a measure ofsimilarity between a pair of words. N-gram techniques:• Digr...
N-gramN-gram techniques:• ( )• Digram (N=2)“• Trigram (N=3)
N-gram The string similarity measures calculated using Dice’sCoefficient:S = 2Cwq /(Aw + Bq)Example :“would be:(2 * 4/(10...
Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Hybrid Method Incorporates three different techniques for Arabic Stemming. The Hybrid algorithm starts with constructing...
Results
Results Hybrid algorithm was found to supersede the otherstemming ones. The obtained results illustrate that using the h...
Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• ...
Conclusion
Thanks
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Arabic tokenization and stemming

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Arabic tokenization and stemming

  1. 1. Imam Mohammad Ibn SaudIslamic UniversityCollege of Computing andInformation ScienceComputer sciences DepartmentPrepared by:Al-Moammar.A., Al-Abdullah.H., and Al-Ajlan.NArabic Tokenization andStemmingSupervised by:Dr. Amal Al-Saif.
  2. 2. Arabic Tokenization andStemming
  3. 3. Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  4. 4. Introduction Arabic language. Tokenization. Stemming.
  5. 5. Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  6. 6. Arabic Language Characteristics• Writing the letter in ambiguous case cause orthography problems.• Encliticization of a word ending with “ ” or “ ” :• Ambiguity results from decliticization of “ ” “l” “ ” “A” and “ ” “Al” “the”.word Encliticization of word“their Friday”“collect them”“Your level”
  7. 7. Outline Introduction Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  8. 8. My Approach Sample of Arabic tokenized text: The Bigrams equation that used is:P(wi | sj) is probability of ith word given jth segmentation.P(sj | si-1)is probability of jth segmentation given previous segmentation.
  9. 9. Outline Introduction Arabic Characteristics. Tokenization:• Arabic Characteristics.• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  10. 10. ResultsThe result of My Approach algorithm:• They used Bigrams on 45 files with size of 29092 tokens.• The final accuracy was 98.83%.Recall Accuracy Precision F-measureResult without statisticalsupport0.9877462 0.9802977 0.8617793 0.920473
  11. 11. Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  12. 12. Arabic Language Characteristics
  13. 13. Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  14. 14. Methodology Root-based. Light Stemmer. N-Gram. Hybrid Method.
  15. 15. Root-based Example of root-based stemmer
  16. 16. Light Stemmer Removed morphemes by Light stemmers
  17. 17. Light Stemmer Classification of Light8 stemmer
  18. 18. N-gram Statistical stemmer based on calculating a measure ofsimilarity between a pair of words. N-gram techniques:• Digram.• Trigram.
  19. 19. N-gramN-gram techniques:• ( )• Digram (N=2)“• Trigram (N=3)
  20. 20. N-gram The string similarity measures calculated using Dice’sCoefficient:S = 2Cwq /(Aw + Bq)Example :“would be:(2 * 4/(10 +5) = 0.533).
  21. 21. Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  22. 22. Hybrid Method Incorporates three different techniques for Arabic Stemming. The Hybrid algorithm starts with constructing the root filecontaining more than 9,000 valid Arabic roots.
  23. 23. Results
  24. 24. Results Hybrid algorithm was found to supersede the otherstemming ones. The obtained results illustrate that using the hybrid stemmerenhances the performance of some Arabic process. In Arabic Text Categorization: the averages accuracies are:74.41% for khoja, 59.71% for light stemming, 48.17% forn-grams, and 82.33% for Hybrid stemmer.
  25. 25. Outline Introduction Arabic Characteristics. Tokenization:• Methodology.• Result. Stemming:• Arabic Characteristics.• Methodology.• Results. Conclusion.
  26. 26. Conclusion
  27. 27. Thanks
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