Author verification of Nahj Al-Balagha

Abstract

The primary purpose of this article is author verification of the Nahj Al-Balagha, a book attributed to Imam Ali and over which Sunni and Shi’i Muslims are proposing different theories. Given the morphologically complex nature of Arabic, we test whether morphological segmentation, applied to the book and works by the two authors suspected by Sunnis to have authored the texts, can be used for author verification of the Nahj Al-Balagha. Our findings indicate that morphological segmentation may lead to slightly better results than whole words and that regardless of the feature sets, the three sub-corpora cluster into three distinct groups using principal component analysis, hierarchical clustering, multi-dimensional scaling, and bootstrap consensus trees. Supervised classification methods such as Naive Bayes, Support Vector Machines, k Nearest Neighbours, Random Forests, AdaBoost, Bagging, and Decision Trees confirm the same results, which is a clear indication that (1) the book is internally consistent and can thus be attributed to a single person and (2) it was not authored by either of the suspected authors.

AGI-P: A Gender Identification Framework for Authorship Analysis Using Customized Fine-Tuning of Multilingual Language Model

“…Moreover, Sarwar et al [1] recently conducted the AGI task on Urdu texts, employing 600 frequent multiword expressions and 300 frequent words as features along with a support vector machine classifier, achieving an accuracy of 93.79%.…”

“…A UTHOR gender identification (AGI) is a task that involves determining the gender of an author based on their writing and has several real-world applications across different fields [1]. For example, in marketing and advertising, understanding the gender of authors can help tailor marketing strategies and advertisements to specific demographics [2].…”

“…The author's gender identification task can be thought of as a binary classification problem. Several features are suggested to perform the author gender identification task such as the most frequent function words, most frequent character n-grams, most frequent word n-grams, most frequent part-of-speech (POS) categories and their n-grams, sentiment lexicon, stylistic markers such as percentage of capital letters or punctuation, and mean sentence length [1], [6]- [25]. To define the target category (male or female), many machine learning classifiers such as Decision Trees, Support Vector Machine, Logistic Regression, K Nearest Neighbors, and Random Forest have been recommended [6]- [9], [14], [15], [26]- [31].…”

“…A significant overseas diaspora speaks Punjabi in the United States, Canada, and the United Kingdom. The Punjabi language has approximately 113 million native speakers 1 .…”