Author Identification Using Chaos Game Representation and Deep Learning

Catalin Stoean and Daniel Lichtblau
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Catalin Stoean: Human Language Technology Research Center, University of Bucharest, 010014 Bucharest, Romania
Daniel Lichtblau: Wolfram Research, Champaign, IL 61820, USA

Mathematics, 2020, vol. 8, issue 11, 1-18

Abstract: An author unconsciously encodes in the written text a certain style that is often difficult to recognize. Still, there are many computational means developed for this purpose that take into account various features, from lexical and character-based attributes to syntactic or semantic ones. We propose an approach that starts from the character level and uses chaos game representation to illustrate documents like images which are subsequently classified by a deep learning algorithm. The experiments are made on three data sets and the outputs are comparable to the results from the literature. The study also verifies the suitability of the method for small data sets and whether image augmentation can improve the classification efficiency.

Keywords: authorship attribution; chaos game representation; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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