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@inproceedings{2369278,
author = {Karásek, Adam and Nevěřilová, Zuzana},
address = {Brno},
booktitle = {Proceedings of the Seventeenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2023},
editor = {Aleš Horák, Pavel Rychlý, Adam Rambousek},
keywords = {authorship identification; evaluation; reproducibility},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1793-7},
pages = {57-65},
publisher = {Tribun EU},
title = {Reproducibility and Robustness of Authorship Identification Approaches},
url = {https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=65},
year = {2023}
}
TY - JOUR
ID - 2369278
AU - Karásek, Adam - Nevěřilová, Zuzana
PY - 2023
TI - Reproducibility and Robustness of Authorship Identification Approaches
PB - Tribun EU
CY - Brno
SN - 9788026317937
KW - authorship identification
KW - evaluation
KW - reproducibility
UR - https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=65
N2 - Authorship identification, framed as a classification task, assigns a digital text to a known author. State-of-the-art algorithms for this task often lack evaluation across diverse datasets. This paper reimplements and evaluates three approaches on three different datasets, exploring the robustness of algorithms on various text types (e.g., emails, articles, instant messages). Not all the published methods are fully reproducible. However, reasonable parameters were selected if they were not part of the original paper. The evaluation of the ensemble model shows it is somewhat robust on different texts and different numbers of potential authors.
ER -
KARÁSEK, Adam and Zuzana NEVĚŘILOVÁ. Reproducibility and Robustness of Authorship Identification Approaches. In Aleš Horák, Pavel Rychlý, Adam Rambousek. \textit{Proceedings of the Seventeenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2023}. Brno: Tribun EU, 2023, p.~57-65. ISBN~978-80-263-1793-7.
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