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Naive Bayes Classifiers For Authorship Attribution of Arabic Texts

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Altheneyan, Alaa Saleh (Author)
مؤلفين آخرين: Menai, Mohamed El Bachir (Co-Author)
المجلد/العدد: مج26, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2014
الصفحات: 473 - 484
DOI: 10.33948/0584-026-004-012
ISSN: 1319-1578
رقم MD: 973394
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Authorship Attribution | Arabic Language | Naive Bayes Classifier | Event Model
رابط المحتوى:
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المستخلص: Authorship attribution is the process of assigning an author to an anonymous text based on writing characteristics. Several authorship attribution methods were developed for natural languages, such as English, Chinese and Dutch. However, the number of related works for Arabic is limited. Naϊve Bayes classifiers have been widely used for various natural language processing tasks. However, there is generally no mention of the event model used, which can have a considerable impact on the performance of the classifier. To the best of our knowledge, naϊve Bayes classifiers have not yet been considered for authorship attribution in Arabic. Therefore, we propose to study their use for this problem, taking into account different event models, namely, simple naϊve Bayes (NB), multinomial naϊve Bayes (MNB), multi-variant Bernoulli naϊve Bayes (MBNB) and multi-variant Poisson naϊve Bayes (MPNB). We evaluate these models’ performances on a large Arabic dataset extracted from books of 10 different authors and compare them with other existing methods. The experimental results show that MBNB provides the best results and could attribute the author of a text with an accuracy of 97.43%. Comparison results with related methods indicate that MBNB and MNB are appropriate for authorship attribution.

ISSN: 1319-1578

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