Article: An intelligent system for author attribution based on a hybrid feature set Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2014 Vol.6 No.4 pp.328 - 345 Abstract: Authorship analysis is a long explored area in the computational research. Recently, there has been growing interest in developing intelligent systems that are capable of authorship identification. Inspired by recent works, we address the problem of author attribution of Arabic text. This area, in specific, has not been targeted in the literature except for few studies. However, it is a challenging problem as there are linguistic complexities associated with the Arabic language including elongation and inflection challenges. For this purpose, we propose a novel hybrid feature set consisting of: lexical, syntactic, structural and content-specific features for 456 instances belonging to seven different Arabic authors. For validation, we run extensive experiments with different intelligent classifiers and show the strength of the proposed feature set. Our results show that the proposed feature set has proved successful with a classification performance accuracy of 88% with the hold-out test and 82% with the cross-validation test. Inderscience Publishers - linking academia, business and industry through research

Title: An intelligent system for author attribution based on a hybrid feature set

Authors: Ahmed Fawzi Otoom; Emad E. Abdallah; Maen Hammad; Mohammad Bsoul; Alaa E. Abdallah

Addresses: Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan

Abstract: Authorship analysis is a long explored area in the computational research. Recently, there has been growing interest in developing intelligent systems that are capable of authorship identification. Inspired by recent works, we address the problem of author attribution of Arabic text. This area, in specific, has not been targeted in the literature except for few studies. However, it is a challenging problem as there are linguistic complexities associated with the Arabic language including elongation and inflection challenges. For this purpose, we propose a novel hybrid feature set consisting of: lexical, syntactic, structural and content-specific features for 456 instances belonging to seven different Arabic authors. For validation, we run extensive experiments with different intelligent classifiers and show the strength of the proposed feature set. Our results show that the proposed feature set has proved successful with a classification performance accuracy of 88% with the hold-out test and 82% with the cross-validation test.

Keywords: authorship attribution; Arabic text features; intelligent classifiers; hybrid feature sets; author identification; classification accuracy.

DOI: 10.1504/IJAIP.2014.066986

International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.4, pp.328 - 345

Received: 05 Apr 2014
Accepted: 16 Sep 2014
Published online: 24 Jan 2015 *

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Title: An intelligent system for author attribution based on a hybrid feature set

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