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Description
  • In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical properties of individual documents or a defined similarity between a document and an author. In this paper, we present the results of an experiment to extend the machine learning attributes by ranking the similarity between a document and an author: we transform the similarity between an unknown document and one of the N authors to the order in which the author is the most similar to the document in the set of N authors. The comparison of similarity probability and similarity ranking was made using the Support Vector Machines algorithm.
  • In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical properties of individual documents or a defined similarity between a document and an author. In this paper, we present the results of an experiment to extend the machine learning attributes by ranking the similarity between a document and an author: we transform the similarity between an unknown document and one of the N authors to the order in which the author is the most similar to the document in the set of N authors. The comparison of similarity probability and similarity ranking was made using the Support Vector Machines algorithm. (en)
Title
  • Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification
  • Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification (en)
skos:prefLabel
  • Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification
  • Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification (en)
skos:notation
  • RIV/00216224:14330/12:00060279!RIV13-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(VF20102014003), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 167881
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/12:00060279
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • authorship identification; machine learning; similarity ranking (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [94A78057E7F6]
http://linked.open...v/mistoKonaniAkce
  • Istanbul (Turkey)
http://linked.open...i/riv/mistoVydani
  • Istanbul (Turkey)
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the Eight International Conference on Language Resources and Evaluation
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Horák, Aleš
  • Rygl, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • European Language Resources Association
https://schema.org/isbn
  • 9782951740877
http://localhost/t...ganizacniJednotka
  • 14330
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