Discriminating the Registers and Styles in the Modern Greek Language-Part 2: Extending the Feature Vector to Optimize Author Discrimination

Tambouratzis, George; Μαρκαντωνάτου, Στέλλα; Hairetakis, Nikolaos; Vassiliou, Marina; Carayannis, G.; Tambouratzis, D. G.

doi:10.1093/llc/19.2.221

Cited by 19 publications

(25 citation statements)

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“…This would involve from simple routines like conversion to lowercase to more complex tools like stemmers (Sanderson & Guenter, 2006), lemmatizers (Tambouratzis, Markantonatou, Hairetakis, Vassiliou, Carayannis, & Tambouratzis, 2004;Gamon, 2004), or detectors of common homographic forms (Burrows, 2002). Another procedure used by van Halteren (2007) is to transform words into an abstract form.…”

Section: Lexical Featuresmentioning

confidence: 99%

“…For example, Tambouratzis, et al (2004) attempted to take advantage of the diglossia phenomenon in Modern Greek and proposed a set of verbal endings which are usually found in 'Katharevousa' and 'Dimotiki', that is, roughly the formal and informal variations of Modern Greek, respectively. Although, such measures have to be defined manually, they can be very effective when dealing with certain text genres.…”

Section: Application-specific Featuresmentioning

confidence: 99%

“…Then, a variety of powerful statistical and machine learning algorithms can be used to build a classification model, including discriminant analysis (Stamatatos, et al, 2000;Tambouratzis, et al, 2004;Chaski, 2005), Support Vector Machines (SVM) (de Vel, et al, 2001;Diederich, et al, 2003;Teng, et al, 2004;Li, et al, 2006;Sanderson & Guenter, 2006), decision trees (Uzuner & Katz, 2005;Zhao & Zobel, 2005;Zheng, et al, 2006), neural networks (Matthews & Merriam, 1993;Merriam & Matthews, 1994;Tweedie, Singh, & Holmes, 1996;Zheng, et al, 2006;Khosmood & Levinson, 2006), genetic algorithms (Holmes & Forsyth, 1995), memory-based learners (Luyckx & Daelemans, 2005), classifier ensemble methods (Stamatatos, 2006a), etc.…”

Section: Vector Space Modelsmentioning

confidence: 99%

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A survey of modern authorship attribution methods

Stamatatos

2008

J. Am. Soc. Inf. Sci.

1,060

822

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Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

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Section: Lexical Featuresmentioning

confidence: 99%

Section: Application-specific Featuresmentioning

confidence: 99%

Section: Vector Space Modelsmentioning

confidence: 99%

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A survey of modern authorship attribution methods

Stamatatos

2008

J. Am. Soc. Inf. Sci.

1,060

822

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“…Examples of this approach can be found in Tambouratzis et al [18] and Chaski [5]. Experimental results show that usually this approach provides better results than the qualitative one.…”

Section: Forensic Stylisticsmentioning

confidence: 99%

Author Identification using Stylometric Features

Pavelec¹,

Justino²,

Oliveira³

2007

Int. Artif.

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In this work we present a strategy for author identification for documents written in Portuguese. It takes into account a writer-independent model which reduces the pattern recognition problem to a single model and two classes, hence, makes it possible to build robust system even when few genuine samples per writer are available. We also introduce a stylometric feature set, which is based on the conjunctions of the Portuguese language. Experiments on a database composed of short articles from 10 different authors and Support Vector Machine (SVM) as classifier demonstrate that the proposed strategy can produced results comparable to the literature.

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“…The main purpose of the present study is to improve the MLP model usage, by adopting an enhanced training and architecture selection procedure, which can also be used for feature ranking and selection. An additional aim is to compare the effectiveness of this procedure to widely used statistical classification methods for author discrimination tasks, such as discriminant analysis [2,8,9]. Hence, for a given set of texts produced by a group of known authors, the aim is to determine the identity of the author of each text.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on authorship attribution classification tasks using both neural network and statistical methods

Tsimboukakis

Tambouratzis

2009

Neural Comput & Applic

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The present paper investigates the application of the multi-layer perceptron (MLP) to the task of categorizing texts based on their authors' style. This task is of particular importance for information retrieval applications involving very large document databases. The emphasis of this article is to determine the extent to which the MLP model can be fine-tuned to successfully analyse such data, uncovering the stylistic differences among authors. The MLP-based method is compared and contrasted to statistical techniques, such as discriminant analysis, that are widely used in stylistic studies. The comparison of the methods is based on their classification performance, to provide an objective evaluation of the advantages of each method. A second aim of the study presented here is to compare the effectiveness of distinct features in the task of uncovering the author identity for each method. To evaluate to a greater depth the effectiveness of the entire approach, the results of the proposed MLP-based method are compared to those of established approaches, such as the support vector machines (SVM), using both the original parameters employed by the MLP as well as term frequency-inverse document frequency (TF-IDF) parameters, and the cascade correlation approach. It is found that the proposed MLP-based approach possesses a number of advantages, such as high classification accuracy, broadly comparable to that of the SVM, coupled with the ability to algorithmically reduce the set of parameters used without adversely affecting the classification accuracy.

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Discriminating the Registers and Styles in the Modern Greek Language-Part 2: Extending the Feature Vector to Optimize Author Discrimination

Cited by 19 publications

References 0 publications

A survey of modern authorship attribution methods

A survey of modern authorship attribution methods

Author Identification using Stylometric Features

A comparative study on authorship attribution classification tasks using both neural network and statistical methods

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