Parallel Stylometric Document Embeddings with Deep Learning Based Language Models in Literary Authorship Attribution

Mihailo Škorić, Ranka Stanković, Milica Ikonić Nešić, Joanna Byszuk and Maciej Eder
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Mihailo Škorić: Faculty of Mining and Geology, University of Belgrade, Djusina 7, 11120 Belgrade, Serbia
Ranka Stanković: Faculty of Mining and Geology, University of Belgrade, Djusina 7, 11120 Belgrade, Serbia
Milica Ikonić Nešić: Faculty of Philology, University of Belgrade, Studentski Trg 3, 11000 Belgrade, Serbia
Joanna Byszuk: Institute of Polish Language, Polish Academy of Sciences, al. Mickiewicza 31, 31-120 Kraków, Poland
Maciej Eder: Institute of Polish Language, Polish Academy of Sciences, al. Mickiewicza 31, 31-120 Kraków, Poland

Mathematics, 2022, vol. 10, issue 5, 1-27

Abstract: 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 embeddings in the form of average, product, minimum, maximum, and l 2 norm of these document embedding matrices and tested them both including and excluding the mBERT-based document embeddings for each language. Finally, we trained several perceptrons on the portions of the dataset in order to procure adequate weights for a weighted combination approach. We tested standalone (two baselines) and composite embeddings for classification accuracy, precision, recall, weighted-average, and macro-averaged F 1 -score, compared them with one another and have found that for each language most of our composition methods outperform the baselines (with a couple of methods outperforming all baselines for all languages), with or without mBERT inputs, which are found to have no significant positive impact on the results of our methods.

Keywords: document embeddings; authorship attribution; language modelling; parallel architectures; stylometry; language processing pipelines (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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