Authorship attribution on Urdu corpus using lexical, syntactic and stylistic features
Master of Science in Computer Science
Faculty / School
Faculty of Computer Sciences (FCS)
Department of Computer Science
Date of Award
Dr. Muhammad Saeed.
Committee Member 1
Dr. Muhammad Saeed, Supervisor, Department of Computer Science, Institute of Business Administration (IBA), Karachi.
MSCS Survey Report
Linguistic stylometry, Syntactic features, Deep learning model.
Abstract / Summary
Authorship attribution has deep roots in linguistic Stylometry. Stylometry is the linguistic information to label documents whose authors are unknown by using the writing style of the possible suspects. Traditional authorship attribution systems rely on the specific vocabulary and writing style of the author. In my study, I have performed two experiments and compare both the experiment and discuss the outcomes. Both experiments examine the performance of the model using the combination of lexical, syntactic and stylistic features. In experiment 1, the performance of Naive Bayes, Support Vector Machine and Random Forest is examined and in experiment 2, the deep learning model is applied and its performance is judge using the traditional approaches in experiment.
Yousuf, S. (2019). Authorship attribution on Urdu corpus using lexical, syntactic and stylistic features (Unpublished survey report). Retrieved from https://ir.iba.edu.pk/research-projects/12
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