Authorship attribution in russian with new high-performing and fully interpretable morpho-syntactic features

Abstract

This work tackles the problem of modeling author style in Russian. In particular, we solve the task of authorship attribution using the collected dataset of 30 authors, 1506 texts written in the period of 18th–21st century. We apply various approaches to solving the attribution problem: Random Forest, Logistic Regression, SVM Classifier. In terms of text representation, we use seven models in three language levels: lexis, morphology, and syntax. Most importantly, we propose our own set of morpho-syntactic features that perform on about the same level as doc2vec, but are fully interpretable. The conducted experiments show the effectiveness of their standalone use, as well as the increase in the quality of classification when using these attributes along with the classic doc2vec-based approach. All code, including feature extraction, is made freely available. Additionally, we analyze the performance of individual features as style markers. Finally, we study classification errors in order to identify the patterns in the misattribution of specific authors.