Shlomo Argamon
ABSTRACT
This paper considers the use of computational stylistics for performing authorship attribution of electronic messages, addressing categorization problems with as many as 20 different classes (authors). Effective stylistic characterization of text is potentially useful for a variety of tasks, as language style contains cues regarding the authorship, purpose, and mood of the text, all of which would be useful adjuncts to information retrieval or knowledge-management tasks. We focus here on the problem of determining the author of an anonymous message, based only on the message text. Several multiclass variants of the Winnow algorithm were applied to a vector representation of the message texts to learn models for discriminating different authors. We present results comparing the classification accuracy of the different approaches. The results show that stylistic models can be accurately learned to determine an author's identity.
- S. Argamon, M. Koppel, J. Fine, and A. R. Shimony. Gender, genre, and writing style in formal written texts. Text, 23(3), 2003.]]Google Scholar
- J. F. Burrows. Computers and the study of literature. In Computers and Written Texts, pages 167--204. Oxford: Blackwell, 1992.]]Google Scholar
- K. Crammer and Y. Singer. Ultraconservative online algorithms for multiclass problems. In Proc. COLT/EuroCOLT, pages 99--115, Amsterdam, 2001.]] Google ScholarDigital Library
- N. Cristianini and J. Shawe-Taylor. An Introduction To Support Vector Machines. Cambridge U. Press, 2000.]] Google ScholarDigital Library
- I. Dagan, Y. Karov, and D. Roth. Mistake-driven learning in text categorization. In Proc. EMNLP-97, Providence, RI.]]Google Scholar
- O. de Vel. Mining e-mail authorship In KDD-2000 Workshop on Text Mining, Boston, MA, 2000.]]Google Scholar
- R. S. Forsyth and D. I. Holmes. Feature finding for text classification. Lit. and Ling. Comp., 11(4):163--174, 1996.]]Google ScholarCross Ref
- S. Har-Peled, D. Roth, and D. Zimak. Constraint classification for multiclass classification and ranking. In NIPS-15, 2002.]]Google Scholar
- D. I. Holmes. The evolution of stylometry in humanities scholarship. Lit. and Ling. Comp., 13(3):111--117, 1998.]]Google ScholarCross Ref
- J. Karlgren. Stylistic Experiments for Information Retrieval. PhD thesis, SICS, 2000.]]Google Scholar
- J. Kivinen and M. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132(1):1--63, 1997.]] Google ScholarDigital Library
- M. Koppel, S. Argamon, and A. R. Shimoni. Automatically categorizing written texts by author gender. Lit. and Ling. Comp., 17(4), 2003.]]Google Scholar
- R. A. J. Matthews and T. V. N. Merriam. Neural computation in stylometry I: An application to the works of Shakespeare and Fletcher. Lit. and Ling. Comp., 8:103--209, 1993.]]Google Scholar
- A. McEnery and M. Oakes. Authorship studies/textual statistics, pages 234--248. Marcel Dekker, 2000.]]Google Scholar
- R. Mitton. Spelling checkers, spelling correctors and the misspellings of poor spellers. Information Processing and Management, 23(5):495--505, 1987.]] Google ScholarDigital Library
- F. Mosteller and D. Wallace. Inference and Disputed Authorship: The Federalist. Addison-Wesley, Reading, Massachusetts, 1964.]]Google Scholar
- F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 2002.]] Google ScholarDigital Library
- E. Stamatatos, N. Fakotakis, and G. Kokkinakis. Automatic text categorisation in terms of genre and author. Comp. Ling., 26(4):471--495, 2001.]] Google ScholarDigital Library
- F. Tweedie, S. Singh, and D. Holmes. Neural network applications in stylometry: The federalist papers. Computers and the Humanities, 30(1):1--10, 1996.]]Google ScholarCross Ref
- M. Wolters and M. Kirsten. Exploring the use of linguistic features in domain and genre classication. In Proc. EACL '99, pages 142--149, 1999.]] Google ScholarDigital Library
- G. U. Yule. Statistical Study of Literary Vocabulary. Cambridge U. Press, 1944.]]Google Scholar
Index Terms
- Style mining of electronic messages for multiple authorship discrimination: first results
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