Multilingual Stylometry. The Influence of Language on the Performance of Authorship Attribution using Corpora from the European Literary Text Collection (ELTeC)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378068%3A_____%2F24%3A00603253" target="_blank" >RIV/68378068:_____/24:00603253 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3834/paper9.pdf" target="_blank" >https://ceur-ws.org/Vol-3834/paper9.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multilingual Stylometry. The Influence of Language on the Performance of Authorship Attribution using Corpora from the European Literary Text Collection (ELTeC)

Popis výsledku v původním jazyce

Stylometric authorship attribution is concerned with the task of assigning texts of unknown, pseudony- mous or disputed authorship to their most likely author, often based on a comparison of the frequency of a selected set of features that represent the texts. The parameters of the analysis, such as feature selec- tion and the choice of similarity measure or classification algorithm, have received significant attention in the past. Two additional key factors for the performance and reliability of stylometric methods, how- ever, have so far received less attention, namely corpus composition and corpus language. As a first step, the aim of this study is to investigate the influence of language on the performance of stylometric authorship attribution. We address this question using four different corpora derived from the European Literary Text Collection (ELTeC). We use machine-translation to obtain each corpus in the other three languages. We find that, as expected, the attribution accuracy varies between language-based corpora, and that translated corpora, on average, display a lower attribution accuracy compared to their counter- parts in the original language. Overall, our study contributes to a better understanding of stylometric methods of authorship attribution.

Název v anglickém jazyce

Multilingual Stylometry. The Influence of Language on the Performance of Authorship Attribution using Corpora from the European Literary Text Collection (ELTeC)

Popis výsledku anglicky

Stylometric authorship attribution is concerned with the task of assigning texts of unknown, pseudony- mous or disputed authorship to their most likely author, often based on a comparison of the frequency of a selected set of features that represent the texts. The parameters of the analysis, such as feature selec- tion and the choice of similarity measure or classification algorithm, have received significant attention in the past. Two additional key factors for the performance and reliability of stylometric methods, how- ever, have so far received less attention, namely corpus composition and corpus language. As a first step, the aim of this study is to investigate the influence of language on the performance of stylometric authorship attribution. We address this question using four different corpora derived from the European Literary Text Collection (ELTeC). We use machine-translation to obtain each corpus in the other three languages. We find that, as expected, the attribution accuracy varies between language-based corpora, and that translated corpora, on average, display a lower attribution accuracy compared to their counter- parts in the original language. Overall, our study contributes to a better understanding of stylometric methods of authorship attribution.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60206 - Specific literatures

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CHR 2024: Computational Humanities Research 2024: Proceedings of the Computational Humanities Research Conference 2024

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

23

Strana od-do

386-408

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Aarhus

Datum konání akce

4. 12. 2024

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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