Abstract
Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.
Original language | English |
---|---|
DOIs | |
Publication status | Published - 1 Sep 2016 |
Event | 9th International Conference on Developments in eSystems Engineering - Liverpool and Leeds, United Kingdom Duration: 31 Aug 2016 → 2 Sep 2016 http://dese.org.uk/dese2016-conference/ |
Conference
Conference | 9th International Conference on Developments in eSystems Engineering |
---|---|
Abbreviated title | DeSE |
Country/Territory | United Kingdom |
City | Liverpool and Leeds |
Period | 31/08/16 → 2/09/16 |
Internet address |
Keywords
- Plagiarism
- Semantics
- Feature extraction
- Tools
- Support vector machines
- Education
- Writing
ASJC Scopus subject areas
- Artificial Intelligence