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Syntactic Dependency-Based N-grams as Classification Features

  • Grigori Sidorov
  • Francisco Velasquez
  • Efstathios Stamatatos
  • Alexander Gelbukh
  • Liliana Chanona-Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7630)

Abstract

In this paper we introduce a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner of what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking the words as they appear in the text. Dependency trees fit directly into this idea, while in case of constituency trees some simple additional steps should be made. Sn-grams can be applied in any NLP task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. SVM classifier for several profile sizes was used. We used as baseline traditional n-grams of words, POS tags and characters. Obtained results are better when applying sn-grams.

Keywords

syntactic n-grams sn-grams parsing classification features syntactic paths authorship attribution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Grigori Sidorov
    • 1
  • Francisco Velasquez
    • 1
  • Efstathios Stamatatos
    • 2
  • Alexander Gelbukh
    • 1
  • Liliana Chanona-Hernández
    • 3
  1. 1.Center for Computing Research (CIC)Instituto Politécnico Nacional (IPN)Mexico CityMexico
  2. 2.University of the AegeanGreece
  3. 3.ESIMEInstituto Politécnico Nacional (IPN)Mexico CityMexico

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