Linguistic correlates of style: authorship classification with deep linguistic analysis features
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Foundations and Trends in Information Retrieval
Computational methods in authorship attribution
Journal of the American Society for Information Science and Technology
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
LTH: semantic structure extraction using nonprojective dependency trees
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Stopword Graphs and Authorship Attribution in Text Corpora
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Probabilistic frame-semantic parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Authorship attribution using probabilistic context-free grammars
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Authorship attribution in the wild
Language Resources and Evaluation
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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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 frame-based 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.