A comparison of several AI techniques for authorship attribution on Romanian texts
Abstract
Determining the author of a text is a difficult task. Here we compare multiple AI techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are Artificial Neural Networks, Support Vector Machines, Multi Expression Programming, Decision Trees with C5.0, and k-Nearest Neighbour. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate decent errors on the test set.
Keywords
Cite
@article{arxiv.2211.05180,
title = {A comparison of several AI techniques for authorship attribution on Romanian texts},
author = {Sanda Maria Avram and Mihai Oltean},
journal= {arXiv preprint arXiv:2211.05180},
year = {2023}
}
Comments
We initially used the Accuracy evaluation tool to compute the macro-accuracy, obtaining a value of 88.84%. We, thereafter discovered that this value was erroneous and used other methods which gave us the value of 80.94% for the macro-accuracy. In this version of the paper we present the python module solution by using sklearn.metrics's classification_report and balanced_accuracy_score