Tuesday , 20 October 2020

Art. 03 – Vol. 25 – No. 3 – 2015

Identifying Research Paper’s Originality Using Intrinsec Plagiarism Analysis

Mădălina ZURINI

Bucharest Academy of Economic Sudies

Abstract: Within the paper, the concept of intellectual property is presented in the context of publishing scientific papers. The level of originality derives from the analyses of intellectual property, a defining component presented in antithesis with the concept of plagiarism. In a further analyses, the main methods of plagiarism identification are presented, concentrating on the intrinsic plagiarism using a proposed metric for evaluating the writing style of an author in accordance to the semantic approach. The proposed metric is tested using a dataset formed out of 17 research articles conducted by an author over 13 years. The interpretation of the results highlights the advantages brought by adding a semantic layer evaluation to the current analysis.

Keywords: stylometry, plagiarism, metrics, originality.


  1. CEDENO, A. B.; VILA, M.; MARTI M. A.; ROSSO, P.: Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection, Computational Linguistics, 39(4), pp. 917-947.
  2. LANCASTER, T.; CULWIN, F.: Classifications of Plagiarism Detection Engines, Available at: http://www-new2.heacademy.ac.uk/assets/documents/subjects/ics/may2005_vol.4_1_classification_plagiarism_detection_engines.pdf
  3. OBERREUTER, G.; L’HUILLER, G.; RIOS, S. A.; VELASQUEZ J. D.: Approaches for Intrinsic and External Plagiarism Detection, Notebook for PAN at CLEF, Available at: http://ceur-ws.org/Vol-1177/CLEF2011wn-PAN-OberreuterEt2011.pdf

View full article

  1. STAMATATOS, E.; KOPPEL, M.: Plagiarism and authorship analysis: introduction to the special issue, Lang Resources & Evaluation, 45(1), 2011, pp. 1-
  2. CARNAHAN, N.; HUDERLE, M.; JONES, N.; STEPHAN, C.; TRAN, T.; WOOD-DOUGHTY Z.: Plagiarism Detection, 2014. Available at: http://www.cs.carleton.edu/cs_comps/1314/dlibenno/final-results/plagcomps.pdf
  3. ALZAHRANI, S. M.; SALIM, N.; ABRAHAM, A.: Understanding Plagiarism Linguistic Patterns, Textual Features and Detection Methods, IEEE Transaction on Systems, Man and Cybernetics – Part C: Applications and Reviews, 42(2), 2012, pp. 133-149.
  4. SALUNKHE, S. D.; GAWALI, S. Z.: A Plagiarism Detection Mechanism using Reinforcement Learning, International Journal of Advance Research in Computer Science and Management Studies, 1(6), 2013, pp. 125-129.
  5. EISSEN, S. M.; STEIN, B.; KULIG, M.: Plagiarism Detection Without Reference Collections, Advances in Data Analysis Studies in Classification, Data Analysis and Knowledge Organization, 2007, pp. 359-366.
  6. STEIN, B.; LIPKA, N.; PRETTENHOFER, P.: Intrinsic plagiarism analysis, Language Resources & Evaluation, 45(1), 2010, pp. 63-82.
  7. SEDDING, J.; KAZAKOV, D.: WordNet-based Text Document Clustering, ROMAND 2004, Workshop on Robust Methods in Analysis of Natural Language Data, Geneva, August, 2004, pp. 104-113.
  8. OSICEANU, M-E.: Consideraţii privind drepturile de proprietate intelectuală în ştiinţă, tehnică şi artă sau între creaţie şi plagiat, Available at: http://api.ning.com/files/uPa7BpseSwF6lqvQmgiaPdijUqzZEL9nHLQzkOJht94wzdjkfubWxs5cGMbkITg3agVjj0s2dOhxhjn88Hy*72*M4OH2MIVb/Osiceanu_MEConsideraiiprivinddrepturiledeproprietateintelectuala_final.pdf

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.