# A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts
## 2 Related Work
[18] conducted another study, which involved training a machine learning model on Arabic Tweets. The dataset used in this study was collected from the Tweets of 45 authors. The aim of this study was to investigate AA for generating authors' writing style profiles. The study showed an accuracy of 99.24% by utilizing TF-IDF vectorizer and the SVM model. [PERSON] et al. [19] conducted a further study, which aimed to predict the authorship of different Arabic Tweets. The researchers collected 150 Tweets for every 1000 users for a total of 1.5 million Tweets. The authors used three classifiers: SVM, KNN, and random forests. Their results showed that the SVM and random forests classifiers performed better with regard to the accuracy measure compared to the KNN classifier. Regarding studies of classical Arabic texts using the Universal Library (Elwaraq), three studies [14; 20; 21] were conducted to test AA within this library. The first of these [20] is a comparative survey conducted by [PERSON] and [PERSON] [20] on old Arabic texts. The dataset was collected from the Elwaraq library, extracted from 10 different books by 10 authors. The researchers created a dataset, which they named the AA of Ancient Arabic Texts (AAAT) set. The study used three different classifiers: multilayer perceptron (MLP), SVM, linear regression, and a proposed new approach called vote-based fusion (VBF). They found that the VBF approach produced a high AA accuracy of 90%, higher than the original classifier score utilizing only one feature. The second study in [21] aimed to limit the noise words from a document to ensure a fair authorship identification process. The researchers constructed a dataset called Authorship Attribution for Ancient Arabic Philosophers (A4P). They investigated character 3 g and words as a feature extraction from the text. Their experiment employed five different classifiers: MLP, SVM, linear regression, Stamatatos distance, and Manhattan distance. This study found that classification performances is mainly based on the utilized features, the classification method, and the level of noise. Thus, the noise limit of 450 words per document appears to be the maximum for obtaining an accurate authorship identification. The third study, by [PERSON] and [PERSON] [14], conducted an experiment with several models of naive Bayes classifiers, simple naive Bayes, multinomial naive Bayes, multivariate Bernoulli naive Bayes, and multivariant Poisson naive Bayes. The dataset was constructed from 30 books by 10 authors ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]) from the Elwaraq website. The authors concluded that the multivariant Bernoulli naive Bayes approach provided the best accuracy at 97%. A distinctive study on classical Arabic texts, which uses another dataset, was conducted by [PERSON] [22]. [PERSON] investigated AA using machine learning methods for classical Arabic texts. The dataset used was derived from OpenITI, which contains 700 books by 20 authors. The experiments utilized three different machine learning classifiers, KNN, logistic regression, and Gaussian Bayes, in a comparative study using different types of style markers based on the different classifiers. This study relied on syntactical information for the experiment, such as function word features, PoS-based features, and character-based features. The findings indicate that these style markers can effectively impact the results of the AA task. In reviewing the published works, we find numerous and considerable studies which address the task of AA for Arabic. However, for classical Arabic, specifically Islamic legal texts, studies are limited. In the following paragraphs, we highlight the studies on the specific Islamic texts subdomain of AA. In 2018, [PERSON] and [PERSON] [39] investigated AA for seven Arabic Islamic books, the Holy Quran, Hadith, Alghazali, Alquaradawi, Abdelkafy, Al-Qarni, and the [PERSON] text collection. To improve the classification performance, they used fusion methods.