Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14)

TL;DR

This paper presents a plagiarism detection method for text-based assignments using a trigram vector space model with cosine similarity, demonstrating improved accuracy over sequence matching techniques.

Contribution

The paper introduces a novel plagiarism detection approach combining trigram vector space model with cosine similarity for better accuracy in identifying intra-corpal plagiarism.

Findings

Trigram vector space model yields better detection results.

Cosine similarity outperforms Jaccard measure in this context.

The proposed tool effectively minimizes student plagiarism.

Abstract

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.