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 contribution

15 Citations (Scopus)

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. © 2013 Springer-Verlag.
Original languageAmerican English
Title of host publicationSyntactic dependency-based n-grams: More evidence of usefulness in classification
Pages13-24
Number of pages10
ISBN (Electronic)9783642372469
DOIs
StatePublished - 3 Apr 2013
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

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

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

Fingerprint

N-gram
Syntactics
Path Following
Parsing
Learning systems
Machine Learning
Syntax
Evidence
Necessary

Cite this

Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., & Chanona-Hernández, L. (2013). Syntactic dependency-based n-grams: More evidence of usefulness in classification. In Syntactic dependency-based n-grams: More evidence of usefulness in classification (pp. 13-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7816 LNCS). https://doi.org/10.1007/978-3-642-37247-6_2
Sidorov, Grigori ; Velasquez, Francisco ; Stamatatos, Efstathios ; Gelbukh, Alexander ; Chanona-Hernández, Liliana. / Syntactic dependency-based n-grams: More evidence of usefulness in classification. Syntactic dependency-based n-grams: More evidence of usefulness in classification. 2013. pp. 13-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Sidorov, G, Velasquez, F, Stamatatos, E, Gelbukh, A & Chanona-Hernández, L 2013, Syntactic dependency-based n-grams: More evidence of usefulness in classification. in Syntactic dependency-based n-grams: More evidence of usefulness in classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7816 LNCS, pp. 13-24, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/14. https://doi.org/10.1007/978-3-642-37247-6_2

Syntactic dependency-based n-grams: More evidence of usefulness in classification. / Sidorov, Grigori; Velasquez, Francisco; Stamatatos, Efstathios; Gelbukh, Alexander; Chanona-Hernández, Liliana.

Syntactic dependency-based n-grams: More evidence of usefulness in classification. 2013. p. 13-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7816 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernández L. Syntactic dependency-based n-grams: More evidence of usefulness in classification. In Syntactic dependency-based n-grams: More evidence of usefulness in classification. 2013. p. 13-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-37247-6_2