An Improved NLP Approach for Detection of Plagiarism in Scientific Paper (original) (raw)
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Plagiarism Detection Algorithm Model Based on NLP Technology
Journal of Cybersecurity and Information Management, 2021
We can bear in mind that each of us has plagiarized a text without realizing that it was plagiarism, Plagiarism can happen in Articles, Papers, Researches, literature, music, software, scientific, newspapers, websites, Master and PHD Thesis and many other fields, So plagiarism has become serious major problem to teachers, researchers and publishers, There are divergent opinions about how to define plagiarism and what makes plagiarism serious. So, the detecting plagiarism is very important, so in this survey we explicate the concept of ;plagiarism ; and provide an overview of different plagiarism software and tools to solve the plagiarism problem, and will discuss the plagiarism process, types and detection methodologies. We can define that plagiarism is the brief and the description of this sentence ;someone used someone else’s mental product (such as its texts, ideas, or privacy). We suggest that what makes plagiarism so reprehensible is that it distorts scientific credit. In addit...
Considerable Issues Over Intelligent Plagiarism Detection Methods
2018
The concept of innovation in doing things in an advanced way at times compels the researcher to commit plagiarism. Plagiarism detection is as yet a troublesome issue in ethical writing practices. Numerous plagiarism detection tools have been made to recognize plagiarism but even the best of the recognized tools cannot identify better than a human eye. In this study, we propose an innovative NLP approach for intelligent plagiarism detection over existing systems. The current research is based on experimenting with an already accepted cumpublished paper into a journal, after being examined by plagiarism detection tool at that journal forum. The innovative approach was able to provide an enhanced originality-check report giving plagiarism type remarks as well, as compared to the one released by the forum. Exploiting both the semantic and structural features makes this approach a hybrid one. Plagiarism guidelines based heuristics have been explored to formulate generic rules of detectin...
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) .
Plagiarism Detection Methods and Tools: An Overview
2021
Plagiarism Detection Systems play an important role in revealing instances of a plagiarism act, especially in the educational sector with scientific documents and papers. The idea of plagiarism is that when any content is copied without permission or citation from the author. To detect such activities, it is necessary to have extensive information about plagiarism forms and classes. Thanks to the developed tools and methods it is possible to reveal many types of plagiarism. The development of the Information and Communication Technologies (ICT) and the availability of the online scientific documents lead to the ease of access to these documents. With the availability of many software text editors, plagiarism detections becomes a critical issue. A large number of scientific papers have already investigated in plagiarism detection, and common types of plagiarism detection datasets are being used for recognition systems, WordNet and PAN Datasets have been used since 2009. The researchers have defined the operation of verbatim plagiarism detection as a simple type of copy and paste. Then they have shed the lights on intelligent plagiarism where this process became more difficult to reveal because it may include manipulation of original text, adoption of other researchers' ideas, and translation to other languages, which will be more challenging to handle. Other researchers have expressed that the ways of plagiarism may overshadow the scientific text by replacing, removing, or inserting words, along with shuffling or modifying the original papers. This paper gives an overall definition of plagiarism and works through different papers for the most known types of plagiarism methods and tools.
Plagiarism Detection Using Artificial Intelligence
International Journal of Computer and Information System (IJCIS), 2024
Presently available plagiarism detection technologies are primarily restricted to string-level comparisons between potentially original texts and suspiciously plagiarized materials. The objective of this research is to enhance the precision of plagiarism identification by integrating Natural Language Processing (NLP) methods into current methodologies. Our proposal is an external plagiarism detection framework that uses various natural language processing (NLP) approaches to examine a set of original and suspicious papers. The techniques not only analyze text strings but also the text's structure, taking text relations into consideration. Preliminary findings using a corpus of short paragraphs that have been plagiarized demonstrate that NLP approach increase the correctness of current methods.
PLAGIARISM DETECTION TECHNIQUES AND LITERATURE SURVEY
International Journal of Computer Engineering and Applications, 2021
Literature is an intellectual knowledge and new arguments are being made for the theft of that literature. New technical tools are being created for this problem. It's said that "Money can be stolen, some goods can be stolen but knowledge cannot be stolen". But by stealing the same knowledge as literature, the theft of the same literature begins with writing on paper. In recent years, many online tools have been able to identify potential plagiarism in research areas. In this paper major contents are the dimensions and techniques of plagiarism, NLP problems of plagiarism identifier, problem of sentences.
Effective detection has been extremely difficult due to plagiarism's pervasiveness throughout a variety of fields, including academia and research. Increasingly complex plagiarism detection strategies are being used by people, making traditional approaches ineffective. The assessment of plagiarism involves a comprehensive examination encompassing syntactic, lexical, semantic, and structural facets. In contrast to traditional string-matching techniques, this investigation adopts a sophisticated Natural Language Processing (NLP) framework. The preprocessing phase entails a series of intricate steps ultimately refining the raw text data. The crux of this methodology lies in the integration of two distinct metrics within the Encoder Representation from Transformers (E-BERT) approach, effectively facilitating a granular exploration of textual similarity. Within the realm of NLP, the amalgamation of Deep and Shallow approaches serves as a lens to delve into the intricate nuances of the text, uncovering underlying layers of meaning. The discerning outcomes of this research unveil the remarkable proficiency of Deep NLP in promptly identifying substantial revisions. Integral to this innovation is the novel utilization of the Waterman algorithm and an English-Spanish dictionary, which contribute to the selection of optimal attributes. Comparative evaluations against alternative models employing distinct encoding methodologies, along with logistic regression as a classifier underscore the potency of the proposed implementation. The culmination of extensive experimentation substantiates the system's prowess, boasting an impressive 99.5% accuracy rate in extracting instances of plagiarism. This research serves as a pivotal advancement in the domain of plagiarism detection, ushering in effective and sophisticated methods to combat the growing spectre of unoriginal content.
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.".
Approach to Text Plagiarism Detection Technique Through Natural Language Processing
Since we move in the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this suffers the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, or help more attention has been directed towards automatic plagiarism detection. This is a computational methodology which assists humans in judging whether pieces of text are plagiarized.
NLP Applications in External Plagiarism Detection
2014
The purpose of our present research is the development of a plagiarism detector, integrating natural language processing tools with similarity measures and n-grams techniques. Our detection target included both verbatim plagiarism and slightly modified passages, in the same language; while the prototype is developed for English documents, the solution can be successfully adapted to other languages. Test results using the prototype over a corpus of documents presented high rates of precision and recall. The current research is in-line with the latest trends in paraphrasing recognition, including high levels of obfuscation, in the quest of uncovering all the forms of plagiarism.