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International Conference on Statistical Language and Speech Processing

SLSP 2019: Statistical Language and Speech Processing pp 299–310Cite as

Authorship Attribution in Russian in Real-World Forensics Scenario

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11816))

Abstract

Recent demands in authorship attribution, specifically, cross-topic authorship attribution with small numbers of training samples and very short texts, impose new challenges on corpora design, feature and algorithm development. In the current work we address these challenges by performing authorship attribution on a specifically designed dataset in Russian. We present a dataset of short written texts in Russian, where both authorship and topic are controlled. We propose a pairwise classification design closely resembling a real-world forensic task. Semantic coherence features are introduced to supplement well-established n-gram features in challenging cross-topic settings. Distance-based measures are compared with machine learning algorithms. The experiment results support the intuition that for very small datasets, distance-based measures perform better than machine learning techniques. Moreover, pairwise classification results show that in difficult cross-topic cases, content-independent features, i.e., part-of-speech n-grams and semantic coherence, are promising. The results are supported by feature significance analysis for the proposed dataset.

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Notes

  1. 1.

    The model is made available for free download by the RusVectōrēs project at https://rusvectores.org/ru/models/.

  2. 2.

    https://scikit-learn.org/.

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Acknowledgment

Authors acknowledge support of this study by the Russian Science Foundation, grant № 18-78-10081 “Modelling of the idiolect of a modern Russian speaker in the context of the problem of authorship attribution”.

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, 16 Soyuza Pechatnikov Street, St. Petersburg, 190121, Russia

    Polina Panicheva

  2. RusProfiling Lab, Voronezh State Pedagogical University, 86 Lenina Street, Voronezh, 394043, Russia

    Polina Panicheva & Tatiana Litvinova

  3. Plekhanov Russian University of Economics, Stremyanny Lane 36, Moscow, 117997, Russia

    Tatiana Litvinova

Authors
  1. Polina Panicheva
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  2. Tatiana Litvinova
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Corresponding author

Correspondence to Polina Panicheva .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. Queen Mary University of London, London, UK

    Matthew Purver

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Senja Pollak

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Panicheva, P., Litvinova, T. (2019). Authorship Attribution in Russian in Real-World Forensics Scenario. In: Martín-Vide, C., Purver, M., Pollak, S. (eds) Statistical Language and Speech Processing. SLSP 2019. Lecture Notes in Computer Science(), vol 11816. Springer, Cham. https://doi.org/10.1007/978-3-030-31372-2_25

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  • DOI: https://doi.org/10.1007/978-3-030-31372-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31371-5

  • Online ISBN: 978-3-030-31372-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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