Determining Window Size from Plagiarism Corpus for Stylometric Features
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Experimental IR Meets Multilinguality, Multimodality, and Interaction |
| MU Faculty or unit | |
| Citation | |
| web | Springer Link |
| Doi | http://dx.doi.org/10.1007/978-3-319-24027-5_31 |
| Field | Informatics |
| Keywords | plagiarism; average word frequency class; stylometry; text classification; intrinsic plagiarism |
| Attached files | |
| Description | 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. |
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