Přehled o publikaci
2015
Determining Window Size from Plagiarism Corpus for Stylometric Features
SUCHOMEL, Šimon and Michal BRANDEJSBasic information
Original name
Determining Window Size from Plagiarism Corpus for Stylometric Features
Authors
SUCHOMEL, Šimon and Michal BRANDEJS
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
Proceedings paper
Field of Study
Informatics
Country of publisher
France
Confidentiality degree
is not subject to a state or trade secret
Publication form
printed version "print"
References:
Marked to be transferred to RIV
Yes
RIV identification code
RIV/00216224:14330/15:00084706
Organization
Fakulta informatiky – Repository – Repository
ISBN
978-3-319-24026-8
ISSN
Keywords in English
plagiarism; average word frequency class; stylometry; text classification; intrinsic plagiarism
Links
LG13010, research and development project.
Changed: 2/9/2020 09:52, RNDr. Daniel Jakubík
Abstract
In the original language
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.
