Determining Window Size from Plagiarism Corpus for Stylometric
Features

D 2015

Determining Window Size from Plagiarism Corpus for Stylometric Features

SUCHOMEL, Šimon and Michal BRANDEJS

Basic information

Original name

Determining Window Size from Plagiarism Corpus for Stylometric Features

Authors

SUCHOMEL, Šimon (203 Czech Republic, belonging to the institution) and Michal BRANDEJS (203 Czech Republic, guarantor, belonging to the institution)

Edition

Toulouse, France, Experimental IR Meets Multilinguality, Multimodality, and Interaction, p. 293-299, 7 pp. 2015

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

France

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

Springer Link

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/15:00084706

Organization unit

Faculty of Informatics

ISBN

978-3-319-24026-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-24027-5_31

UT WoS

000364677800034

Keywords in English

plagiarism; average word frequency class; stylometry; text classification; intrinsic plagiarism

Abstract

V originále

The sliding window concept is a common method for computing a profile of a document with unknown structure. This paper outlines an experiment with stylometric word-based feature in order to determine an optimal size of the sliding window. It was conducted for a vocabulary richness method called ‘average word frequency class’ using the PAN 2015 source retrieval training corpus for plagiarism detection. The paper shows the pros and cons of the stop words removal for the sliding window document profiling and discusses the utilization of the selected feature for intrinsic plagiarism detection. The experiment resulted in the recommendation of setting the sliding windows to around 100 words in length for computing the text profile using the average word frequency class stylometric feature.

Links

LG13010, research and development project

Name: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Acronym: ERCIM-CZ)

Investor: Ministry of Education, Youth and Sports of the CR

Files attached

Determining_Window_Size_from_Plagiarism_Corpus_for_Stylometric_Features.pdf

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Citovat

SUCHOMEL, Šimon and Michal BRANDEJS. Determining Window Size from Plagiarism Corpus for Stylometric Features. In Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, GarethJ.F. and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola. Experimental IR Meets Multilinguality, Multimodality, and Interaction. Toulouse, France: Springer International Publishing, 2015, p. 293-299. ISBN 978-3-319-24026-8. Available from: https://dx.doi.org/10.1007/978-3-319-24027-5_31.

@inproceedings{1317554,
   author = {Suchomel, Šimon and Brandejs, Michal},
   address = {Toulouse, France},
   booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
   doi = {http://dx.doi.org/10.1007/978-3-319-24027-5_31},
   editor = {Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, GarethJ.F. and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola},
   keywords = {plagiarism; average word frequency class; stylometry; text classification; intrinsic plagiarism},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Toulouse, France},
   isbn = {978-3-319-24026-8},
   pages = {293-299},
   publisher = {Springer International Publishing},
   title = {Determining Window Size from Plagiarism Corpus for Stylometric Features},
   url = {http://link.springer.com/chapter/10.1007%2F978-3-319-24027-5_31},
   year = {2015}
}

TY  - JOUR
ID  - 1317554
AU  - Suchomel, Šimon - Brandejs, Michal
PY  - 2015
TI  - Determining Window Size from Plagiarism Corpus for Stylometric Features
PB  - Springer International Publishing
CY  - Toulouse, France
SN  - 9783319240268
KW  - plagiarism
KW  - average word frequency class
KW  - stylometry
KW  - text classification
KW  - intrinsic plagiarism
UR  - http://link.springer.com/chapter/10.1007%2F978-3-319-24027-5_31
L2  - http://link.springer.com/chapter/10.1007%2F978-3-319-24027-5_31
N2  - The sliding window concept is a common method for computing a profile of a document with unknown structure. This paper outlines an experiment with stylometric word-based feature in order to determine an optimal size of the sliding window. It was conducted for a vocabulary richness method called ‘average word frequency class’ using the PAN 2015 source retrieval training corpus for plagiarism detection. The paper shows the pros and cons of the stop words removal for the sliding window document profiling and discusses the utilization of the selected feature for intrinsic plagiarism detection. The experiment resulted in the recommendation of setting the sliding windows to around 100 words in length for computing the text profile using the average word frequency class stylometric feature.
ER  -

SUCHOMEL, Šimon and Michal BRANDEJS. Determining Window Size from Plagiarism Corpus for Stylometric Features. In Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, GarethJ.F. and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola. \textit{Experimental IR Meets Multilinguality, Multimodality, and Interaction}. Toulouse, France: Springer International Publishing, 2015, p.~293-299. ISBN~978-3-319-24026-8. Available from: https://dx.doi.org/10.1007/978-3-319-24027-5\_{}31.

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