A Survey on Plagiarism Detection Systems

Bin-Habtoor, A. S.; Zaher, Mahmoud A.

doi:10.7763/ijcte.2012.v4.447

Cited by 38 publications

(27 citation statements)

References 16 publications

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“… Replacing the original author's name  Copying ideas, phrases, concepts, research proposals, articles, reports, computer program designs, websites, and the internet and other electronic resources without citing the author's name  Lack of citation regarding quotation  False referencing or referencing the nonexisting resources  Translation plagiarism, where the translated text is submitted without reference to the original text  Artistic plagiarism, where different media including images and videos are used for other works without (a) proper reference(s) to the resource(s) [2,4]. There are two major methods that can be used to reduce literary pirating: plagiarism detection and plagiarism prevention [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Plagiarism checker for Persian (PCP) texts using hash-based tree representative fingerprinting

Rafieian

Dastjerdi

2016

JAIDM

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With due respect to the authors' rights, plagiarism detection is one of the critical problems in the field of text-mining, in which many researchers are interested. This issue has been considered as a serious one in high academic institutions. There exist language-free tools that do not yield any reliable results since the special features of every language are ignored in them. Considering the paucity of works in the field of Persian language due to the lack of reliable plagiarism checkers in Persian, there is a need for a method to improve the accuracy of detecting plagiarized Persian phrases. An attempt is made in this work to present the PCP solution. This solution is a combinational method, in which, in addition to the meaning and stem of words, synonyms and pluralization are dealt with by applying the document tree representation based on manner fingerprinting the text in the 3-grams words. The grams obtained are eliminated from the text, hashed through the BKDR hash function, and stored as the fingerprint of a document in fingerprints of the reference document repositories in order to check the suspicious documents. The proposed PCP method here is evaluated by eight experiments on seven different sets, which include the suspicions documents and the reference document from the Hamshahri newspaper website. The results obtained indicate that the accuracy of this proposed method in detecting similar texts, in comparison with the "Winnowing" localized method, has a 21.15% average improvement. The accuracy of the PCP method in detecting the similarities, in comparison with the language-free tool, reveals a 31.65% average improvement.

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Section: Introductionmentioning

confidence: 99%

“…There are two major methods that can be used to reduce literary pirating: plagiarism detection and plagiarism prevention [3,4]. An attempt is made in this work to adapt the detection method.…”

Section: Introductionmentioning

confidence: 99%

Plagiarism checker for Persian (PCP) texts using hash-based tree representative fingerprinting

Rafieian

Dastjerdi

2016

JAIDM

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“…Semantic similarity received less attention for the inherent difficulties of representing semantics and the limitations on assessment coverage of user studies [54] [72]. Commonly used methods for determination of similarity include fingerprinting [21], Information Retrieval [28] and other hybrid techniques [24] [44]. In Information Retrieval models, more emphasis was put on representing documents by their words and word frequencies.…”

Section: Related Workmentioning

confidence: 99%

“…The multiple lingualism of such data explosion further necessitates the need for more robust, efficient and generalized tools and techniques to facilitate the utilization of available content. Text representations and processing have become an important backbone of many tools and applications including text mining [5] [6], summarization [7]- [17], clustering [18], categorization [19] [20], copy-detection [21] [22] [23], plagiarism [24] [25], web-search [26] [27], information retrieval [28] and computational biology [29] [30] [31].…”

Section: Introductionmentioning

confidence: 99%

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

Elhadi¹

2017

IJIS

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This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text's sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet; and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then re-cycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.

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“…A survey on plagiarism detection [12] outlined the application of attribute counting techniques based on average line length, file size, and average number of commas per line to derive file fingerprints. The resulting fingerprint is then used to estimate the degree of plagiarism.…”

Section: Related Researchmentioning

confidence: 99%

Plagiarism Index Estimation Algorithm: A Quantitative Approach

Eze¹,

Kamboh²

2015

IJCA

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Plagiarism has remained a serious setback especially in the academia. It is a major source of intellectual theft since it gives credits for scientific innovations to those who do not merit them. A number of efforts have been made by researchers to tackle plagiarism. However, one perceived research gap is the need to evolve verifiable computational techniques for detecting and quantifying the degree of plagiarism in digitized documents. This current research tackles this problem through a specialized plagiarism detection and quantification algorithm. It begins with a bipartitioned search operation known as F-Search. This is followed by a purge operation which excludes the plagiarized sections discovered during the initial pass, thus giving rise to a fresh search space. The resulting search space is passed through a more thorough search operation known as T-Search. At this stage, the algorithm deals with specific plagiarism hiding tricks termed as whitespace flooding. The final output is a statistic known as the Plagiarism Index, which is a numeric value in the range [0, 1] for estimating the degree of plagiarism. The scope of this research covers the text domain. Each experimental dataset is made up of a set of two documents designed in such a way that one is assumed as the original document, while the second as a plagiarized copy. The system is designed and implemented in MATLAB.

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A Survey on Plagiarism Detection Systems

Cited by 38 publications

References 16 publications

Plagiarism checker for Persian (PCP) texts using hash-based tree representative fingerprinting

Plagiarism checker for Persian (PCP) texts using hash-based tree representative fingerprinting

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

Plagiarism Index Estimation Algorithm: A Quantitative Approach

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