Authorship Attribution with Support Vector Machines

By Joachim Diederich, Jörg Kindermann, Edda Leopold, Gerhard Paass. Applied intelligence (Boston), 2003

402 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/314c1fb6988f42429963480f3167568253bd0ad7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

Twitter Sentiment Analysis (2015, background): “It has been shown that the use of n-grams can improve the quality of text classiﬁcation (Raskutti et al., 2001; Zhang, 2003; Diederich et al., 2003), however there is no unique solution for the the size of n-gram.”
A survey of modern authorship attribution methods (2009, methodology): “…classification model, including discriminant analysis (Chaski, 2005; Stamatatos et al., 2000; Tambouratzis et al., 2004), SVM (de Vel et al., 2001; Diederich et al, 2003; Li et al., 2006; Sanderson & Guenter, 2006; Teng et al., 2004), decision trees (Uzuner & Katz, 2005; Zhao & Zobel, 2005; Zheng…”
Probabilistic author-topic models for information discovery (2004, methodology): “More recent work of a similar nature includes authorship analysis of a purported poem by Shakespeare (Thisted and Efron, 1987), identifying authors of software programs (Gray, Sallis, and MacDonell, 1997), and the use of techniques such as support vector machines (Diederich et al., 2003) for author ”
Syntactic N-grams as machine learning features for natural language processing (2014, methodology): “On the other hand, the instance-based methods require multiple training text samples per author in order to develop an accurate attribution model [18, 22].”
Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “researchers have used POS tag frequencies or POS tag n-gram frequencies to represent style (Argamon-Engelson, Koppel & Avneri, 1998; Kukushkina, Polikarpov, & Khmelev, 2001; Koppel & Schler, 2003; Diederich, Kindermann, Leopold, & Paass, 2003, Gamon, 2004; Zhao & Zobel, 2007).”
Computational methods in authorship attribution (2009, neutral): “Such patterns of lexical choice can be represented by modeling the relative frequencies of content words (Martindale and McKenzie 1995; Craig 1999; Waugh et al. 2000; Diederich et al. 2003; Hoover 2004a, 2004b; Argamon et al. 2008).”
Authorship verification as a one-class classification problem (2004, background): “…include Naïve Bayes (Mosteller and Wallace 1964, Peng et al 2004), which for the two-class case is a linear separator, Winnow and Exponential Gradient (Lewis et al 1996, Dagan et al 1997, Koppel et al 2002) and linear support vector machines (Joachims 1999, de Vel et al 2002, Diederich et al 2003).”
A framework for authorship identification of online messages: Writing-style features and classification techniques (2006, methodology): “Due to the fact that an SVM is capable of handling millions of inputs and good performance (Cristianini & Shawe-Taylor, 2000; Joachims, 2002), it was introduced to authorship-analysis research in previous work (Argamon, 2003; de Vel et al., 2001; Diederich et al., 2000).”
Learning author-topic models from text corpora (2010, methodology): “1997], and the use of techniques such as neural networks [Kjell 1994] and support vector machines [Diederich et al. 2003] for author identification.”