Abstract
Authorship identification was introduced as one of the important problems in the law and journalism fields and it is one of the major techniques in plagiarism detection. In this paper, to tackle the authorship verification problem, we propose a probabilistic distribution model to represent each document as a feature set to increase the interpretability of the results and features. We also introduce a distance measure to compute the distance between two feature sets. Finally, we exploit a KNN-based approach and a dynamic feature selection method to detect the features which discriminate the author’s writing style.
The experimental results on PAN at CLEF 2013 dataset show the effectiveness of the proposed method. We also show that feature selection is necessary to achieve an outstanding performance. In addition, we conduct a comprehensive analysis on our proposed dynamic feature selection method which shows that discriminative features are different for different authors.
A simplified version of the approach proposed in this paper participated in PAN at CLEF 2014 Authorship Identification competition. In PAN 2014, we did not consider knee detection technique for feature selection and only selected the best two features. It is worth mentioning that the achieved results on English Novels and Dutch Reviews datasets were promising.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)
Forner, P., Navigli, R., Tufis, D. (eds.): CLEF 2013 Evaluation Labs and Workshop–Working Notes Papers (2013)
Genkin, A., Lewis, D.D., Madigan, D.: Large-scale bayesian logistic regression for text categorization. Technometrics 49, 291–304 (2007)
Graham, N., Hirst, G., Marthi, B.: Segmenting documents by stylistic character. Nat. Lang. Eng. 11(4), 397–415 (2005)
Halvani, O., Steinebach, M., Zimmermann, R.: Authorship verification via k-nearest neighbor estimation - notebook for pan at clef 2013. In: Forner et al [2]
Joula, P., Stamatatos, E.: Overview of the author identification task at pan 2013. In: Information Access Evaluation. Multilinguality, Multimodality, and Visualization. vol. 8138 (2013)
Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics 22, 49–86 (1951)
Li, J., Zheng, R., Chen, H.: From fingerprint to writeprint. Commun. ACM 49(4), 76–82 (2006)
Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computational Linguistics, COLING 2008, pp. 513–520 (2008)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Mohtasseb, H., Ahmed, A.: Two-layered blogger identification model integrating profile and instance-based methods. Knowl. Inf. Syst. 31(1), 1–21 (2012)
Potha, N., Stamatatos, E.: A profile-based method for authorship verification. In: Likas, A., Blekas, K., Kalles, D. (eds.) SETN 2014. LNCS, vol. 8445, pp. 313–326. Springer, Heidelberg (2014)
Sanderson, C., Guenter, S.: Short text authorship attribution via sequence kernels, markov chains, and author unmasking: An investigation. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP 2006, pp. 482–491 (2006)
Seidman, S.: Authorship verification using the impostors method - notebook for pan at clef 2013. In: Forner et al. [2]
Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538–556 (2009)
Stamatatos, E., Koppel, M.: Plagiarism and authorship analysis: introduction to the special issue. Language Resources and Evaluation 45(1), 1–4 (2011)
Zhao, Y., Zobel, J.: Searching with style: Authorship attribution in classic literature. In: Proceedings of the Thirtieth Australasian Conference on Computer Science, ACSC 2007, pp. 59–68 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zamani, H., Esfahani, H.N., Babaie, P., Abnar, S., Dehghani, M., Shakery, A. (2014). Authorship Identification Using Dynamic Selection of Features from Probabilistic Feature Set. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-11382-1_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11381-4
Online ISBN: 978-3-319-11382-1
eBook Packages: Computer ScienceComputer Science (R0)