Determining the authorship of literary works using neural networks (based on French poetry)

Abstract

Determining the authorship of literary works using neural networks (based on French poetry)

Metelkova L.A.., Sak A.N.

Incoming article date: 30.09.2024

This paper discusses statistical methods, as well as machine learning methods for choosing the optimal way to establish authorship for a passage of a work. The authors create a dataset from the passages of the corresponding authors, create a set of numerical features corresponding to each passage and apply various approaches to analyze authorship, such as correlation, similarity, t-test. An attempt is made to find the optimal method for the output layer of a graph convolutional neural network used for data preprocessing. The GCN neural network is being trained.

Keywords: t-test, cosine similarity, correlation, graph convolutional neural networks, natural language analysis