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.
Highlights
The style of every author is encoded into the documents that the person has written, be that these are books, articles, or emails or some small statement in a social network
A large data set like IMDb can be used for observing how the proposed chaos game representation (CGR)-deep learning (DL) tandem performs with such data sets
The proposed CGR-DL reached an accuracy of 82% on the test set in the best configuration found in validation without any augmentation
Summary
The style of every author is encoded into the documents that the person has written, be that these are books, articles, or emails or some small statement in a social network. The style gets more consistent as the amount of text written is larger. The authorship attribution (AA) task assumes that there is a training set of documents for which the authors are known and another test set of texts where the writer is not acknowledged in advance but these are, in general, written by one of the authors from the training samples. The goal is to determine who is the author for each of the test samples. Most computational approaches deal with counting of various components in the texts for determining the style of the writer and, the author. A good survey for such methods can be read in Reference [1] or in an overview for a competition in AA [2]
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