Syntactic dependency-based n-grams: More evidence of usefulness in classification

Grigori Sidorov, Francisco Velasquez, Efstathios Stamatatos, Alexander Gelbukh, Liliana Chanona-Hernández

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

23 Scopus citations

Abstract

The paper introduces and discusses a concept of syntactic n-grams (sn-grams) that can be applied instead of traditional n-grams in many NLP tasks. Sn-grams are constructed by following paths in syntactic trees, so sn-grams allow bringing syntactic knowledge into machine learning methods. Still, previous parsing is necessary for their construction. We applied sn-grams in the task of authorship attribution for corpora of three and seven authors with very promising results.

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 14th International Conference, CICLing 2013, Proceedings
Pages13-24
Number of pages12
EditionPART 1
DOIs
StatePublished - 2013
Event14th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013 - Samos, Greece
Duration: 24 Mar 201330 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7816 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013
Country/TerritoryGreece
CitySamos
Period24/03/1330/03/13

Keywords

  • SVM classifier
  • Syntactic n-grams
  • authorship attribution task
  • sn-grams
  • syntactic paths

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