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Literary and Linguistic Computing 2004 19(2):221-242; doi:10.1093/llc/19.2.221
© 2004 by Association for Literary & Linguistic Computing
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Discriminating the Registers and Styles in the Modern Greek Language-Part 2: Extending the Feature Vector to Optimize Author Discrimination

George Tambouratzis1, Stella Markantonatou1, Nikolaos Hairetakis1, Marina Vassiliou1, George Carayannis1 and Dimitrios Tambouratzis2

1 Institute for Language and Speech Processing, Greece 2 Agricultural University of Athens, Greece

This article describes a method for discriminating among authors within a given register of Modern Greek. The focus here is to determine to what extent the stylistic differences among authors can be detected with a high degree of accuracy for a set of texts belonging to a well-defined register. To that end, the chosen register is characterized by a well-defined sub-language, from which a corpus of more than 1,000 documents has been created. To discriminate the texts according to author style, a series of experiments have been performed using statistical techniques. Each text has been represented by a vector covering several linguistic aspects, in an effort to determine the most effective style markers. The experimental results indicate that the proposed approach can successfully separate the author styles for a given register. An extensive study of the effectiveness of the different variable categories has been performed. For instance, diglossia information on its own is not sufficient for author discrimination. Instead, a systematic evaluation process indicates that part-of-speech, structural and algorithmically derived lemma-frequency variables are the most important style markers, their use leading to an author discrimination accuracy exceeding 90%.


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G. Tambouratzis and M. Vassiliou
Employing Thematic Variables for Enhancing Classification Accuracy Within Author Discrimination Experiments
Lit Linguist Computing, June 1, 2007; 22(2): 207 - 224.
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