Detailed Information on Publication Record

@inproceedings{19266,
   author = {Suchomel, Šimon and Brandejs, Michal},
   address = {Toulouse, France},
   booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
   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  - 19266
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
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 \textit{Experimental IR Meets Multilinguality, Multimodality, and Interaction}. Toulouse, France: Springer International Publishing, 2015, p.~293-299. ISBN~978-3-319-24026-8.