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  1. Data from: PAN19 Authorship Analysis : Cross-Domain Authorship Attribution

    • repository.uantwerpen.be
    • zenodo.org
    Updated 2019
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Kestemont, Mike; Stamatatos, Efstathios; Manjavacas, Enrique; Daelemans, Walter; Potthast, Martin; Stein, Benno (2019). PAN19 Authorship Analysis : Cross-Domain Authorship Attribution [Dataset]. https://repository.uantwerpen.be/link/irua/169090
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Data from: PAN19 Authorship Analysis : Cross-Domain Authorship Attribution

Related Article
Dataset updated
2019
Dataset provided by
University of Antwerphttps://www.uantwerp.be/en
Faculty of Arts. Linguistics
Faculty of Arts. Literature
Authors
Kestemont, Mike; Stamatatos, Efstathios; Manjavacas, Enrique; Daelemans, Walter; Potthast, Martin; Stein, Benno
Description

Authorship attribution is an important problem in information retrieval and computational linguistics but also in applied areas such as law and journalism where knowing the author of a document (such as a ransom note) may enable e.g. law enforcement to save lives. The most common framework for testing candidate algorithms is the closed-set attribution task: given a sample of reference documents from a restricted and finite set of candidate authors, the task is to determine the most likely author of a previously unseen document of unknown authorship. This task may be quite challenging in cross-domain conditions, when documents of known and unknown authorship come from different domains (e.g., thematic area, genre). In addition, it is often more realistic to assume that the true author of a disputed document is not necessarily included in the list of candidates.

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