Language Independent Authorship Attribution with Character Level N-Grams

Language Independent Authorship Attribution using Character Level Language Models Fuchun Pen& Dale Schuurmanst Vlado Kesel11 Shaojun Wan& School of Computer Science, University of Waterloo, Canada ff3peng, dale, sjwangl@cs.uwaterloo.ca IFaculty of Computing Science, Dalhousie University, Canada vlado@cs . dal . ca Abstract We present a method for computer- assisted authorship attribution based on character-level n-gram language mod- els. Our approach is based on sim- ple information theoretic principles, and achieves improved performance across a variety of languages without requir- ing extensive pre-processing or feature selection. To demonstrate the effec- tiveness and language independence of our approach, we present experimen- tal results on Greek, English, and Chi- nese data. We show that our approach achieves state of the art performance in each of these cases. In particular, we ob- tain a 18% accuracy improvement over the best published results for a Greek data set, while using a far simpler tech- nique than previous investigations. 1 Introduction Automated authorship attribution is the problem of identifying the author of an anonymous text, or text whose authorship is in doubt (Love, 2002). A famous example is the Federalist Papers, of which twelve are claimed to have been written both by Alexander Hamilton and James Madison (Holmes and Forsyth, 1995). Recently, vast repos- itories of electronic text have become available on the Internet, making the problem of managing large text collections increasingly important. Au- tomated text categorization (TC) is a useful way to organize a large document collection by impos- ing a desired categorization scheme. For exam- ple, categorizing documents by their author is an important case that has become increasingly use- ful, but also increasingly difficult in the age of web-documents that can be easily copied, trans- lated and edited. Author attribution is becoming an important application in web information man- agement, and is beginning to play a role in areas such as information retrieval, information extrac- tion and question answering. Many algorithms have been invented for as- sessing the authorship of given text. These al- gorithms rely on the fact that authors use lin- guistic devices at every level—semantic, syntac- tic, lexicographic, orthographic and morphologi- cal (Ephratt, 1997)—to produce their text. Typi- cally, such devices are applied unconsciously by the author, and thus provide a useful basis for un- ambiguously determining authorship. The most common approach to determining authorship is to use a stylistic analysis that proceeds in two steps: first, specific style markers are extracted, and sec- ond, a classification procedure is applied to the resulting description. These methods are usually based on calculating lexical measures that repre- sent the richness of the author's vocabulary and the frequency of common word use (Stamatatos et al., 2001). Style marker extraction is usually accom- plished by some form of non-trivial NLP analysis, such as tagging, parsing and morphological analy- sis. A classifier is then constructed, usually by first performing a non-trivial feature selection step that employs mutual information or Chi-square testing 267 to determine relevant features. There are several problems with this standard approach however. …

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