Spectral Profiling of Writing Process

Natalia Kizhaeva, Zeev Volkovich, Oleg Granichin, Olga Granichina, Vladimir Kiyaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper discusses a novel methodology for dynamic modeling of writing process. Sequent sub-documents of a given document are described through occurrences of the suitably selected N-grams. The Mean Dependence similarity measures the association between a present sub-document and numerous preceding ones and transforms a document into a time series, which is supposed to be weak stationary if the document is created using the same writing style. A periodogram of this signal estimates its Power Spectral Density providing a spectral attribute of the style. Numerical experiments demonstrate high ability of the proposed method in authorship identification and the reveal of writing style evolution.

Original languageEnglish
Title of host publication2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)
PublisherIEEE Canada
Pages2063-2068
Number of pages6
StatePublished - 2017
Event1st Annual IEEE Conference on Control Technology and Applications - Hawaii, United States
Duration: 27 Aug 201730 Aug 2017

Conference

Conference1st Annual IEEE Conference on Control Technology and Applications
CountryUnited States
Period27/08/1730/08/17

Keywords

  • Writing style
  • Authorship Attribution
  • Spectral attributing

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