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CitePlag: A Citation-based plagiarism detection system prototype

This paper presents an open-source prototype of a citation-based plagiarism detection system called CitePlag. The underlying idea of the system is to evaluate the citations of academic documents as language independent markers to detect plagiarism. CitePlag uses three different detection algorithms that analyze the citation sequence of academic documents for similar patterns that may indicate unduly used foreign text or ideas. The algorithms consider multiple citation-related factors such as proximity and order of citations within the text, or their probability of co-occurrence in order to compute document similarity scores. We present technical details of CitePlag's detection algorithms and the acquisition of test data from the PubMed Central Open Access Subset. Future advancement of the prototype lies in increasing the reference database by enabling the system to process more document and citation formats. Improving CitePlag's detection algorithms and scoring functions to reduce the number of false positives is another major goal. Eventually, we plan to integrate text-based detection algorithms in addition to the citation-based detection algorithms within CitePlag.

Citation-based Plagiarism Detection–Idea, Implementation and Evaluation

Currently used Plagiarism Detection Systems solely rely on textbased comparisons. They only deliver satisfying results if the plagiarized text is copied literally (copy&paste), with minor alterations (e.g. shake&paste) or is machine translated. However, if the text is paraphrased or translated by a human, the currently used methods yield a very poor performance. Using the words of Weber Wulff, who organizes regular comparisons for Plagiarism Detection Systems (PDS), the current state of available systems can be summarized as follows: "[…] PDS find copies, not plagiarism.".

Reuse and plagiarism in Speech and Natural Language Processing publications

International Journal on Digital Libraries, 2017

The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy & paste operations between articles in the domain of Natural Language Processing, including Speech Processing (NLP). The search space of the comparisons is a corpus labelled as NLP4NLP, which includes 34 different sources and gathers a large part of the publications in the NLP field over the past 50 years. This study considers the similarity between the papers of each individual source and the complete set of papers in the whole corpus, according to four different types of relationship (self-reuse, self-plagiarism, reuse and plagiarism) and in both directions: a source paper borrowing a fragment of text from another paper of the collection, or in the reverse direction, fragments of text from the source paper being borrowed and inserted in another paper of the collection.

THE METHOD FOR DETECTING PLAGIARISM IN A COLLECTION OF DOCUMENTS

Supervises of doctoral students (PhD level). Under my guidance was protected the next PhD dissertations: Noha Roman. Methods and tools for text analysis publications to identify and study the functioning scientific schools: Applied and Mathematical Linguistics, 2015 Melnikova Natalia. Automated processing personalized medical information for decision support systems: information technologies, 2014 Pshnychnyy Oleksandr. Mathematical and detection software conjunction associative dependencies in relational databases: mathematical and soft-ware, 2013 Vovk Olena. Methods and means of improving sustainability website as an information product: information technologies, 2013 Current research interests: Big Data, Database and datawarehouse integration, distributed systems, integrated systems and dataspaces. I have published more than 170 scientific papers, 3 monographs, 4 textbooks. Technical Leader of several information systems embedded in Lviv Polytechnic and other corporations in Ukraine

INAOE at DEFT 2011: Using a Plagiarism Detection Method for Pairing Abstracts-Scientific Papers

Cet article décrit la méthode développée par le Laboratoire de Technologies Langagières de l'IN-AOE pour la tâche d'appariement résumés/articles dans le cadre de DEFT 2011. Pour aborder cette tâche, on a présupposé qu'un auteur emploi les mêmes expressions contenues dans le corps d'un article pour construire le résumé respectif. En conséquence, notre méthode cherche à retrouver les parties de texte réutilisé dans les résumés et les articles afin de déterminer le degré de dérivation entre eux. Notre méthode suit une stratégie non-supervisée qui ne dépend d'aucune ressources linguistiques, ce qui permet à notre méthode d'être générale et indépendante de la langue. Les résultats obtenus indiquent que le calcul de degré de dérivation entre deux documents peut être utilisé pour ce type de tâches.

Citation Analysis as a Basis for the Development of an Additional Module in Antiplagiarism Systems

Scientific and Technical Information Processing, 2013

The use of lists of references in different areas is growing exponentially. Serving as basis for defining the relationships between authors, publications, and journals, references gave an impetus to the development of a set of indicators and indices and thus had a large impact on the development of bibliometrics and scientometrics. In this short paper the use of references as an additional module of an anitplagiarism system is suggested. It is shown how direct borrowings from scientific works can be identified by comparing bibliographies in view of their unique structure, which varies from one publication to another.

Google search: A simple and free tool to detect plagiarism

Indian Journal of Vascular and Endovascular Surgery, 2018

The method of searching plagiarism with the help of Google's “exact word or phrase” search can be used by authors, reviewers, and editors to check duplicate content present in the manuscript. However, this method has limitations. It may not be capable of detecting an inadequately paraphrased sentence. Hence, its usage should be adapted with caution.

Hybrid System for Plagiarism Detection on A Scientific Paper

2021

Plagiarism Detection Systems are critical in identifying instances of plagiarism, particularly in the educational sector whenever it comes to scientific publications and papers. Plagiarism occurs when any material is copied without the author's consent or attribution. To identify such acts, thorough knowledge of plagiarism types and classes is required. It is feasible to detect several sorts of plagiarism using current tools and methodologies. With the advancement of information and communication technologies (ICT) and the availability of online scientific publications, access to these publications has grown more convenient. Additionally, with the availability of several software text editors, plagiarism detection has become a crucial concern. Numerous scholarly articles have previously examined plagiarism detection and the two most often used datasets for plagiarism detection, WordNet and the PAN Dataset. The researchers described verbatim plagiarism detection as a straightforward case of copying and pasting, and then shed light on clever plagiarism, which is more difficult to detect since it may involve original text alteration, borrowing ideas from other studies, and Other scholars have said that plagiarism can obscure the scientific content by substituting terms, deleting or introducing material, rearranging or changing the original publications. The suggested system incorporated natural language processing (NLP) and machine learning (ML) techniques, as well as an external plagiarism detection strategy based on text mining and similarity analysis. The suggested technique employs a mix of Jaccard and cosine similarity. It was examined using the PAN-PC-11 corpus. The proposed system outperforms previous systems on the PAN-PC-11, as demonstrated by the findings. Additionally, the proposed system obtains an accuracy of 0.96, a recall of 0.86, an F-measure of 0.86, and a PlagDet score of 0.86. (0.86). 0.865 and the proposed technique is substantiated by a design application that is used to detect plagiarism in scientific publications and generate nonmedication notifications. Portable Document Format (PDF) .