Publication details

Automatic Adaptation of Author's Stylometric Features to Document Types



Year of publication 2014
Type Article in Proceedings
Conference Text, Speech, and Dialogue - 17th International Conference
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords authorship verification; feature selection; machine learning; stylome; stylometric features
Description 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).
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