Abstract
Authorship attribution is the task of deciding who wrote a particular document. Several attribution approaches have been proposed in recent research, but none of these approaches is particularly satisfactory; some of them are ad hoc and most have defects in terms of scalability, effectiveness, and efficiency. In this paper, we propose a principled approach motivated from information theory to identify authors based on elements of writing style. We make use of the Kullback-Leibler divergence, a measure of how different two distributions are, and explore several different approaches to tokenizing documents to extract style markers. We use several data collections to examine the performance of our approach. We have found that our proposed approach is as effective as the best existing attribution methods for two class attribution, and is superior for multi-class attribution. It has lower computational cost and is cheaper to train. Finally, our results suggest this approach is a promising alternative for other categorization problems.
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References
Baayen, H., Halteren, H.V., Neijt, A., Tweedie, F.: An experiment in authorship attribution. In: 6th JADT (2002)
Baayen, H., Halteren, H.V., Tweedie, F.: Outside the cave of shadows: Using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing 11(3), 121–132 (1996)
Benedetto, D., Caglioti, E., Loreto, V.: Language trees and zipping. The American Physical Society 88(4) (2002)
Binongo, J.N.G.: Who wrote the 15th book of oz? an application of multivariate statistics to authorship attribution. Computational Linguistics 16(2), 9–17 (2003)
Diederich, J., Kindermann, J., Leopold, E., Paass, G.: Authorship attribution with support vector machines. Applied Intelligence 19(1-2), 109–123 (2003)
Fung, G.: The disputed federalist papers: Svm feature selection via concave minimization. In: Proceedings of the 2003 Conference on Diversity in Computing, pp. 42–46. ACM Press, New York (2003)
Goodman, J.: Extended comment on language trees and zipping (1995)
Harman, D.: Overview of the second text retrieval conference (TREC-2). Information Processing & Management 31(3), 271–289 (1995)
Heckerman, D., Geiger, D., Chickering, D.: Learning bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Holmes, D.I., Robertson, M., paez, R.: Stephen crane and the new-york tribune: A case study in traditional and non-traditional authorship attribution. Computers and the Humanities 35(3), 315–331 (2001)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Juola, P., Baayen, H.: A controlled-corpus experiment in authorship identification by cross-entropy. Literary and Linguistic Computing (2003)
Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: Pasific Association for Computational Linguistics, pp. 256–264 (2003)
Khmelev, D.V., Tweedie, F.J.: Using markov chains for identification of writers. Literary and Linguistic Computing 16(4), 229–307 (2002)
Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Twenty-first International Conference on Machine Learning. ACM Press, New York (2004)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Manning, C., Schze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Peng, F., Schuurmans, D., Keselj, V., Wang, S.: Language independent authorship attribution using character level language models. In: 10th Conference of the European Chapter of the Association for Computational Linguistics, EACL (2003)
Peng, F., Schuurmans, D., Wang, S.: Language and task independent text categorization with simple language models. In: NAACL 2003: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Association for Computational Linguistics, Morristown, NJ, USA, pp. 110–117 (2003)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. The MIT Press, Cambridge (2002)
Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic authorship attribution. In: Proceedings of the 9th Conference of the European Chapter of the Association for Computational Linguistics, pp. 158–164 (1999)
Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Computer-based authorship attribution without lexical measures. Computers and the Humanities 35(2), 193–214 (2001)
Vapnik, V., Wu, D.: Support vector machine for text categorization (1998)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004)
Zhao, Y., Zobel, J.: Effective authorship attribution using function word. In: 2nd Asian Information Retrieval Symposium, pp. 174–190. Springer, Heidelberg (2005)
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Zhao, Y., Zobel, J., Vines, P. (2006). Using Relative Entropy for Authorship Attribution. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_8
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DOI: https://doi.org/10.1007/11880592_8
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