Abstract
In this paper we introduce a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner of what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking the words as they appear in the text. Dependency trees fit directly into this idea, while in case of constituency trees some simple additional steps should be made. Sn-grams can be applied in any NLP task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. SVM classifier for several profile sizes was used. We used as baseline traditional n-grams of words, POS tags and characters. Obtained results are better when applying sn-grams.
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References
Khalilov, M., Fonollosa, J.A.R.: N-gram-based Statistical Machine Translation versus Syntax Augmented Machine Translation: comparison and system combination. In: Proceedings of the 12th Conference of the European Chapter of the ACL, pp. 424–432 (2009)
Habash, N.: The Use of a Structural N-gram Language Model in Generation-Heavy Hybrid Machine Translation. In: Belz, A., Evans, R., Piwek, P. (eds.) INLG 2004. LNCS (LNAI), vol. 3123, pp. 61–69. Springer, Heidelberg (2004)
Agarwal, A., Biads, F., Mckeown, K.R.: Contextual Phrase-Level Polarity Analysis using Lexical Affect Scoring and Syntactic N-grams. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL), pp. 24–32 (2009)
García-Hernández, R.A., Martínez Trinidad, J.F., Carrasco-Ochoa, J.A.: Finding Maximal Sequential Patterns in Text Document Collections and Single Documents. Informatica 34(1), 93–101 (2010)
Baayen, H., Tweedie, F., Halteren, H.: Outside The Cave of Shadows: Using Syntactic Annotation to Enhance Authorship Attribution. Literary and Linguistic Computing, 121–131 (1996)
Stamatatos, E.: A survey of modern authorship attribution methods. Journal of the American Society for information Science and Technology 60(3), 538–556 (2009)
Holmes, D.I.: The evolution of stylometry in humanities scholarship. Literary and Linguistic Computing 13(3), 111–117 (1998)
Juola, P.: Authorship Attribution. Foundations and Trends in Information Retrieval 1(3), 233–334 (2006)
Juola, P.: Ad-hoc authorship attribution competition. In: Proceedings of the Joint Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing, pp. 175–176 (2004)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002)
Abbasi, A., Chen, H.: Applying authorship analysis to extremist-group web forum messages. IEEE Intelligent Systems 20(5), 67–75 (2005)
van Halteren, H.: Author verification by linguistic profiling: An exploration of the parameter space. ACM Transactions on Speech and Language Processing 4(1), 1–17 (2007)
Grieve, J.: Quantitative authorship attribution: An evaluation of techniques. Literary and Linguistic Computing 22(3), 251–270 (2007)
Luyckx, K.: Scalability Issues in Authorship Attribution. Ph.D. Thesis, University of Antwerp (2010)
Argamon, S., Juola, P.: Overview of the international authorship identification competition at PAN-2011. In: 5th Int. Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (2011)
Diederich, J., Kindermann, J., et al.: Authorship attribution with support vector machines. Applied Intelligence 19(1), 109–123 (2003)
Escalante, H., Solorio, T., et al.: Local histograms of character n-grams for authorship attribution. In: 49th Annual Meeting of the Association for Computational Linguistics, pp. 288–298 (2011)
Keselj, V., Peng, F., et al.: N-gram-based author profiles for authorship attribution. Computational Linguistics 3, 225–264 (2003)
Koppel, M., Schler, J., et al.: Authorship attribution in the wild. Language Resources and Evaluation 45(1), 83–94 (2011)
Koppel, M., Schler, J., et al.: Measuring differentiability: unmasking pseudonymous authors. Journal of Machine Learning Research, 1261–1276 (2007)
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Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L. (2013). Syntactic Dependency-Based N-grams as Classification Features. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_1
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DOI: https://doi.org/10.1007/978-3-642-37798-3_1
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