Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14)
This addresses plagiarism detection for academic assignments, but is incremental as it applies existing methods (vector space models) to a specific domain.
The paper tackles plagiarism detection in text-based assignments by comparing unigram, bigram, and trigram vector space models with cosine similarity, finding that trigram with cosine similarity performs best, though slower, and slightly outperforms a trigram sequence matching technique with Jaccard measure.
Plagiarism is known as illegal use of others' part of work or whole work as one's own in any field such as art, poetry, literature, cinema, research and other creative forms of study. Plagiarism is one of the important issues in academic and research fields and giving more concern in academic systems. The situation is even worse with the availability of ample resources on the web. This paper focuses on an effective plagiarism detection tool on identifying suitable intra-corpal plagiarism detection for text based assignments by comparing unigram, bigram, trigram of vector space model with cosine similarity measure. Manually evaluated, labelled dataset was tested using unigram, bigram and trigram vector. Even though trigram vector consumes comparatively more time, it shows better results with the labelled data. In addition, the selected trigram vector space model with cosine similarity measure is compared with tri-gram sequence matching technique with Jaccard measure. In the results, cosine similarity score shows slightly higher values than the other. Because, it focuses on giving more weight for terms that do not frequently exist in the dataset and cosine similarity measure using trigram technique is more preferable than the other. Therefore, we present our new tool and it could be used as an effective tool to evaluate text based electronic assignments and minimize the plagiarism among students.