# A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts
## 2 Related Work
Their experiment used an artificial neural network model with five-fold cross-validation to compare machine learning models, such as Bernoulli naive Bayes, SVM, DT, and random forests. Their results indicate that using the artificial neural network outperformed other classifiers, using different metrics such as F-score, accuracy, precision, and recall. Nevertheless, these studies by [PERSON] et al. are restricted to Islamic rulings only. Focusing on ontologies, a study by [PERSON] et al. [41] approached the task of AA with a model that uses Arabic ontology with semantic features to attribute a ruling to its jurisprudence doctrine (legal school). The authors introduced a new dataset, \"ElWafaaa LIFcahaa\", which is considered a valuable contribution. The results of evaluating the proposed model produced an accuracy of 90%. Another investigation was conducted in [25] for Islamic school AA. The purpose of the study was to examine how stylometric features can be used to predict the attribution of a given text to a legal Islamic school. The study used two approaches: unsupervised cluster analysis and supervised machine learning techniques. The dataset consisted of 135 books from the four Islamic schools, Hanbali, Hanafi, Shafi'T, and Maliki. The results indicate SVM achieved the best performance, with an accuracy of 97%. Nevertheless, both studies are constrained to Islamic school attribution and do not address the task of attributing an Arabic Islamic legal text to a specific author. [PERSON] et al. [42] demonstrated how research into AA was mainly concerned with authorship and time attribution of Arabic and Islamic texts. Their project examined some factors that may affect writing style, such as genre and time of composition. The data were collected from a new dataset called CLAUDia for works written between the 9 th and 11 th centuries. The texts they investigated included multiple genres, such as Arabic literature, Islamic text, and linguistics. The experiment focused on testing the effect of character n-grams, word n-grams, the most common words, rare words, and the length of the word as well. The results indicate that the established attribution graphs seem to contain one central vertex that demonstrates the prototypical author. Regarding Islamic text in this study, it is limited to eight authors, and uses a standard authorship analysis tool (JGAAP). Upon reviewing the literature, we find that a limited number of studies have incorporated AA for Islamic texts. Although some of these have achieved significant results, they have not utilized pretrained transformer models. In addition, the Islamic legal text dataset is novel and has not been used in any previous studies, particularly with transformers. To summarize the review of the relevant work, AA is a well-established task in the natural language processing literature. However, compared to other languages, relatively few studies examine AA for Arabic, and fewer still investigate it for the genre of Islamic texts. Surveying the literature shows that 85% of Arabic AA research papers have used either statistical methods or traditional machine learning-based approaches [15; 16; 19; 28]. The traditional machine learning classifiers such as SMO-SVM, linear regression, and MLP achieved a high level of accuracy, as in [6], which reached 100%. In contrast, the KNN machine learning classifier achieved a low level of accuracy of 35% in [19]. One study, [40], used a deep learning approach by using an artificial neural network for the task of Arabic AA. However, no existing studies have used transformer- or BERT-based models for Islamic legal text AA.