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Automatic Adaptation of Author’s Stylometric Features to Document Types

  • Jan Rygl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

Many Internet users face the problem of anonymous documents and texts with a counterfeit authorship. The number of questionable documents exceeds the capacity of human experts, therefore a universal automated authorship identification system supporting all types of documents is needed. In this paper, five predominant document types are analysed in the context of the authorship verification: books, blogs, discussions, comments and tweets. A method of an automatic selection of authors’ stylometric features using a double-layer machine learning is proposed and evaluated. Experiments are conducted on ten disjunct train and test sets and a method of an efficient training of large number of machine learning models is introduced (163,700 models were trained).

Keywords

authorship verification feature selection machine learning stylome stylometric features 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jan Rygl
    • 1
  1. 1.Natural Language Processing Centre, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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