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• W2868306385 abstract "Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used." @default.
• W2868306385 created "2018-07-19" @default.
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• W2868306385 date "2018-08-01" @default.
• W2868306385 modified "2023-10-06" @default.
• W2868306385 title "Topic or Style? Exploring the Most Useful Features for Authorship Attribution" @default.
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