Lost in translation: authorship attribution using frame semantics
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Lost in translation : authorship attribution using frame semantics. / Hedegaard, Steffen; Simonsen, Jakob Grue.
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers. Bind 2 Association for Computational Linguistics, 2011. s. 65-70.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Lost in translation
T2 - 49th Annual Meeting of the Association for Computational Linguistics
AU - Hedegaard, Steffen
AU - Simonsen, Jakob Grue
N1 - Conference code: 49
PY - 2011
Y1 - 2011
N2 - We investigate authorship attribution using classifiers based on frame semantics. The purpose is to discover whether adding semantic information to lexical and syntactic methods for authorship attribution will improve them, specifically to address the difficult problem of authorship attribution of translated texts. Our results suggest (i) that frame-based classifiers are usable for author attribution of both translated and untranslated texts; (ii) that framebased classifiers generally perform worse than the baseline classifiers for untranslated texts, but (iii) perform as well as, or superior to the baseline classifiers on translated texts; (iv) that—contrary to current belief—naïve classifiers based on lexical markers may perform tolerably on translated texts if the combination of author and translator is present in the training set of a classifier.
AB - We investigate authorship attribution using classifiers based on frame semantics. The purpose is to discover whether adding semantic information to lexical and syntactic methods for authorship attribution will improve them, specifically to address the difficult problem of authorship attribution of translated texts. Our results suggest (i) that frame-based classifiers are usable for author attribution of both translated and untranslated texts; (ii) that framebased classifiers generally perform worse than the baseline classifiers for untranslated texts, but (iii) perform as well as, or superior to the baseline classifiers on translated texts; (iv) that—contrary to current belief—naïve classifiers based on lexical markers may perform tolerably on translated texts if the combination of author and translator is present in the training set of a classifier.
M3 - Article in proceedings
SN - 978-1-932432-88-6
VL - 2
SP - 65
EP - 70
BT - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics
Y2 - 19 June 2011 through 24 June 2011
ER -
ID: 37441108