Investigating the impact of combined similarity metrics and POS tagging in extrinsic text plagiarism detection system

• Published in: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 1578 - 1584
• Main Authors: Vani, K., Gupta, Deepa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2015
• Subjects: Combined Metrics; Extrinsic Plagiarism; Feature extraction; Measurement; Plagiarism; POS tagging; Semantics; Single Metrics; Speech; Tagging; Training; Vector Space Model
• ISBN: 9781479987900; 1479987905
• DOI: 10.1109/ICACCI.2015.7275838

Plagiarism is an illicit act which has become a prime concern mainly in educational and research domains. This deceitful act is usually referred as an intellectual theft which has swiftly increased with the rapid technological developments and information accessibility. Thus the need for a system/ mechanism for efficient plagiarism detection is at its urgency. In this paper, an investigation of different combined similarity metrics for extrinsic plagiarism detection is done and it focuses on unfolding the importance of combined similarity metrics over the commonly used single metric usage in plagiarism detection task. Further the impact of utilizing part of speech tagging (POS) in the plagiarism detection model is analyzed. Different combinations of the four single metrics, Cosine similarity, Dice coefficient, Match coefficient and Fuzzy-Semantic measure is used with and without POS tag information. These systems are evaluated using PAN 1 -2014 training and test data set and results are analyzed and compared using standard PAN measures, viz, recall, precision, granularity and plagdet_score.