Lost in translation: Authorship attribution using frame semantics
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Lost in translation : Authorship attribution using frame semantics. / Hedegaard, Steffen; Simonsen, Jakob Grue.
ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011. p. 65-70 (ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Lost in translation
T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
AU - Hedegaard, Steffen
AU - Simonsen, Jakob Grue
PY - 2011/12/1
Y1 - 2011/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=84859042203&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:84859042203
SN - 9781932432886
T3 - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
SP - 65
EP - 70
BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Y2 - 19 June 2011 through 24 June 2011
ER -
ID: 224020667