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الإسم : مي النشاشيبي

الرتبة العلمية: أستاذ مساعد

المسمى الوظيفي: عضو هيئة تدريسية

المكتب 7301       الرقم الفرعي 7301

بريد الكتروني: mnashashibi@uop.edu.jo

التخصص: علم الحاسوب

جامعة التخرج: جامعة برادفورد

المؤهل العلمي

    المؤهل العلمي

    الجامعة

    البلد

    سنة الحصول على المؤهل

    البكالوريوس
    جامعة الكويت
    الكويت
    1986
    الماجستير
    جامعة الكويت
    الكويت
    1990
    الدكتوراه
    جامعة برادفورد
    المملكه المتحدة
    2013

مقرر لجنة الدراسات والإحصاءات - كلية تكنولوجيا العلومات- جامعة البترا -  ٢٠١٦-٢٠١٨



  • Conference paper





      Y. Swan, K. Botros, , " Improved High Tc Superconductivity above 125oK in Y-Ba-Cu-O System " , "American Physical Society Meeting",Vol.,No., , , 03/01/1989 :الملخص
      Improved High Tc Superconductivity above 125oK in Y-Ba-Cu-O System




      May Y. Al-Nashashibi, " An improved root extraction technique for Arabic words " , "The second International Conference on Computer Technology and Development",Vol.,No., IEEE, Cairo, Egypt, 11/02/2010 :الملخص
      algorithm to handle weak, eliminated-long-vowel, hamzated, and geminated words since the linguistic approach does not handle such cases and a reasonably large portion of Arabic words in texts are irregular. The accuracy of the extracted roots is determined by comparing them with a predefined list of 5,405 triliteral and quadriliteral roots. The linguistic approach performance (with and without the proposed correction algorithm) was tested on an in-house text collection of eight categories. The proposed correction algorithm improved the accuracy of the linguistic one by about 14%.




      May Y. Al-Nashashibi, " Stemming Techniques for Arabic Words: A Comparative Study " , "The Second International Conference on Computer Technology and Development",Vol.,No., IEEE, Cairo, Egypt, 11/02/2010 :الملخص
      Text interpretation depends among other things on a pre-processing stage in extracting effectively a correct stem or root. Since there is no available standard stemmer for Arabic, we address here five methods for extracting Arabic roots and the outcomes of the approach with best results will be used later on. Four of these methods are based on a positional-letter-ranking approach where such an approach is investigated along with an adjustment, and two proposed variants. The fifth one is a rule-based approach. An algorithm for correcting irregular words is applied for all methods and a comparison is made between all approaches. The accuracy of these methods was found by comparing extracted roots with a predefined list of roots using an in-house text collection. Results show that the correction algorithm improved the accuracy of the rule-based one by about 14% and the positional letter ranking based algorithms by 7% to 10%. The adjusted positional letter ranking method proved to be the highest in accuracy among all five algorithms but slightly higher than the rule-based one. However, the rule-based algorithm was found to be the approach with the highest accuracy among all ten algorithms when the correction algorithm was included in it.


  • Web





      May Y. Al-Nashashibi, " 20th meeting of Computational Linguistics In the Netherlands - Universiteit Utrecht - Poster Session " , "CLIN 20",Vol.,No., , , 02/05/2010 :الملخص
      Arabic text interpretation depends among other things on a pre-processing stage in extracting a correct stem or root. We address in this work a linguistic approach for root extraction as a pre-processing step for Arabic text mining. The linguistic approach is composed of a rule-based light stemmer and a pattern-based infix remover. Since this approach does not handle defective, vocalized words and slightly handles geminated words, we propose an algorithm to handle such cases by using 5737 possible correction cases in 71 predefined lists. This algorithm is proposed since there is reasonably large portion of Arabic words that are defective and most available stemmers of Arabic words either don't handle such words or handle them poorly. The accuracy of the extracted roots is determined by comparing them with a predefined list of 5404 triliteral and quadriliteral roots. The linguistic approach performance (with and without the proposed correction algorithm)was tested on an original text collection of eight categories. The accuracies of the linguistic algorithm along with the proposed correction one are reported here. The proposed correction algorithm improved the performance of the linguistic one by about 14%. Download
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