This paper explores the effectiveness of parallel stylometric document embeddings in solving the authorship attribution task by testing a novel approach on literary texts in 7 different languages, totaling in 7051 unique 10,000-token chunks from 700 PoS and lemma annotated documents. We used these documents to produce four document embedding models using Stylo R package (word-based, lemma-based, PoS-trigrams-based, and PoS-mask-based) and one document embedding model using mBERT for each of the seven languages. We created further derivations of these ...
Mihailo Škorić, Ranka Stanković, Milica Ikonić Nešić, Joanna Byszuk, Maciej Eder. "Parallel Stylometric Document Embeddings with Deep Learning Based Language Models in Literary Authorship Attribution" in Mathematics, MDPI AG (2022). https://doi.org/10.3390/math10050838
