Abstract
In this paper, we conduct an investigation of automatic authorship attribution on seven Arabic religious books, namely: the holy Quran, Hadith and five other books, by using two fusion techniques. The Arabic dialect is the same (i.e. Standard Arabic) for the seven books. The genre is the same and the topic of the different books is also the same (i.e. Religion).
The authorship characterization is based on four different features: character trigrams, character tetragrams, word unigrams and word bigrams. The task of authorship identification is ensured by four conventional classifiers: Manhattan distance, Multi-Layer Perceptron, Support Vector Machines and Linear Regression. Furthermore, we propose two fusion approaches to strengthen the classification performances. Finally, a particular application is dedicated to the authorship discrimination between the Quran and Hadith, in order to see if the two books could have the same Author or not. Results have shown the importance of the fusion techniques in authorship attribution and confirm that the two books (Quran and Hadith) should belong to two different Authors, which implies that the Quran could not be written by the Prophet.
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Sayoud, H., Hadjadj, H. (2018). Fusion Based Authorship Attribution-Application of Comparison Between the Quran and Hadith. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds) Arabic Language Processing: From Theory to Practice. ICALP 2017. Communications in Computer and Information Science, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-319-73500-9_14
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DOI: https://doi.org/10.1007/978-3-319-73500-9_14
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