Abstract
Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.
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References
Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
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)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (May 1999)
Benedetto, D., Caglioti, E., Loreto, V.: Language trees and zipping. The American Physical Society 88(4) (2002)
Bernstein, Y., Zobel, J.: A scalable system for identifying co-derivative documents. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 55–67. Springer, Heidelberg (2004)
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)
Burrows, J.: Word patterns and story shapes: the statistical analysis of narrative style. Literary and linguistic Computing 2, 61–70 (1987)
Burrows, J.: Delta: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing 17, 267–287 (2002)
Diederich, J., Kindermann, J., Leopold, E., Paass, G.: Authorship attribution with support vector machines. Applied Intelligence 19(1-2), 109–123 (2003)
D’Souza, D., Thom, J., Zobel, J.: Collection selection for managed distributed document databases. Information Processing & Management 40, 527–546 (2004)
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
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)
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publisher, San Francisco (1995)
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)
Langley, P., Sage, S.: Tractable average-case analysis of naive Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 220–228. Morgan Kaufmann Publisher, San Francisco (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)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (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)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
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Zhao, Y., Zobel, J. (2005). Effective and Scalable Authorship Attribution Using Function Words. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_14
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DOI: https://doi.org/10.1007/11562382_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29186-2
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