Plagiarism detection using natural language processing techniques

N. Malik; A. Bilal; M. Ilyas; S. Razzaq; F. Maqbool; Q. Abbas

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Plagiarism detection using natural language processing techniques

Author(s)

N. Malik Department of Computer Science, Virtual University of Pakistan,Jinnah Campus, Lahore,Pakistan

A. Bilal School of Electrical Engineering and Computer Science,NUST, Islamabad,Pakistan

M. Ilyas Department of Computer Science and Information Technology, University of Sargodha, Sargodha

S. Razzaq Department of Computer Science and Information Technology, University of Sargodha, Sargodha

F. Maqbool Department of Computer Science and Information Technology, University of Sargodha, Sargodha

Q. Abbas Department of Computer Science and Information Technology, University of Sargodha, Sargodha

Abstract

Nowadays, plagiarism became very common in many fields of life, such as research and educational fields. Due to the advancement in plagiarism techniques adopted by plagiarists, it is challenging to detect plagiarism accurately by the existing techniques. Different features are observed while checking plagiarism, such as syntactic, lexical, semantic, and structural features. This research explores new and modern plagiarism detection tasks especially, text-based plagiarism detection including monolingual plagiarism detection. We proposed a four-stage novel framework for plagiarism detection. This framework uses natural Language Processing (NLP) instead of focusing on traditional string-matching approaches. This framework explores text similarity by combining two metrics as Skip-Gram and Dice Coefficient, on the corpus-based approach. The Deep meaning of the text is explored by the use of the Deep and shallow NLP technique. Our results conclude that Heavy revision is identifying quickly through Deep NLP. Shallow NLP prepares text very well that is processed further quickly. Word2vec results are close to simple Deep NLP methods, but word2vec also highlights those documents that other techniques may not highlight. Synonym and phrase changes are also captured through Deep NLP.

Publication Details

Page(s) 90-102

DOI DOI not available

Published Journal: Technical Journal, Volume: 26, Issue: 1, Year: 2021

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Keywords

Word2vec, natural language processing, Deep NLP, - Skip-Gram, Shallow NLP, word embedding