Authorship Attribution in Russian with New High-Performing and Fully Interpretable Morpho-Syntactic Features

P. 193–204.

Pimonova E., Durandin O., Malafeev A.

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.

Keywords: машинное обучение, natural language processing, автоматическая обработка естественного языка, machine learning, authorship attribution, авторский стиль, Text representation, text classification, Author Style, morpho-syntactic features, language feature engineering, определение авторства, классификация текста, формальное представление текста, морфосинтаксические признаки, разработка языковых признаков

In book

Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers

Vol. 11832. , Cham: Springer, 2019.