Plagiarism Detection in Marathi Language Using Semantic Analysis

Naik, Ramesh R.; Landge, Maheshkumar B.; Mahender, C. Namrata

doi:10.4018/ijsita.2017100103

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Paraphrasing detection tools can be expanded to identify copying from others' work ideas. There is a great deal of published research literature, mainly in the discipline of computer science, that can be used to identify translation plagiarism or paraphrasing (Kent & Salim, 2010;Mustofa & Sir, 2013;Naik et al, 2015;Tlitova et al, 2020).…”

Section: Findings From the Doctoral Students' Perspectivementioning

confidence: 99%

Plagiarism in the Research Reports of Indian Doctoral Students: Causes and a Remedial Action Plan

Pradhan,

Kumar

2023

CEPSj

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Many reputable academic journals have retracted research papers from Indian researchers because of plagiarism. The University Grant Commission, a representative organisation of the Indian government, is diligently endeavouring to ensure academic integrity by applying stringent guidelines. The present study aims to find the potential causes of the plagiarism found in the research reports of Indian doctoral students and to formulate a remedial action plan. A literature review was undertaken to identify incidences of plagiarism at educational institutions. Based on the review’s insights, a survey was conducted to investigate doctoral students’ awareness of plagiarism, including causes and remedial action plans. In addition, 21 interviews were conducted with senior academics and professionals from various academic disciplines to gain an understanding of their viewpoints. An analysis was then undertaken of the responses received through the questionnaires and interviews. The results suggested the widespread incidence of plagiarism and shed light on its causes. A remedial action plan emerged from the study, which included 1) establishing a research ethics committee at all academic or research institutions, 2) fostering a correct understanding of plagiarism and its implications by conducting training, workshops and awareness campaigns at an early stage of doctoral students’ lives, 3) ensuring clarity of research purpose among doctoral students and emphasising the quality of research work, 4) developing academic writing skills, and 5) making anti-plagiarism software available free of charge to all students and faculty members. Indian students perceive the University Grant Commission’s stringent guidelines as a good initiative. However, these guidelines cannot be implemented fruitfully without addressing the underlying causes of plagiarism.

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Section: Findings From the Doctoral Students' Perspectivementioning

confidence: 99%

Plagiarism in the Research Reports of Indian Doctoral Students: Causes and a Remedial Action Plan

Pradhan,

Kumar

2023

CEPSj

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“…The degree of matching between two text pieces based on their statistics as well as semantics is termed as the similarity between text pieces [24]. Statistics of the document means the length of the document, tokens present in the document, etc.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Similarity between the Sanskrit Documents using the Context of the Corpus

Saini¹,

Prafulla²

2020

IJACSA

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Identifying the similarity between two documents is a challenging but important task. It benefits various applications like recommender systems, plagiarism detection and so on. To process any text document one of the popularly used approaches is document term matrix (DTM). The proposed approach processes the oldest, untouched, one of the morphologically critical languages, Sanskrit and builds a document term matrix for Sanskrit (DTMS) and Document synset matrix Sanskrit (DSMS). DTMS uses the frequency of the term whereas DSMS uses the frequency of synset instead of term and contributes to the dimension reduction. The proposed approach considers the semantics and context of the corpus to solve the problem of polysemy. More than 760 documents including Subhashitas and stories are processed together. F1 Score, precision, Matthews Correlation coefficient (MCC) which is the most balanced measure and accuracy are used to prove the betterment of the proposed approach.

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“…Plagiarism detection has been defined as the process of extracting features of a document, analyzing its content, and identifying portions that might be plagiarized by bringing evidence of similar source documents (Alzahrani et al, 2012). According to Naik et al (2015), plagiarism detection approaches can be classified into two main groups namely external, and intrinsic detection methods. The key differences between these approaches are clearly discussed in the subsequent paragraphs of this section.…”

Section: Approaches To Plagiarism Detectionmentioning

confidence: 99%

Development of an Algorithm for Plagiarism Detection

Nkotagu¹

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For many years, plagiarism has remained a serious problem in Higher Learning Institutions (HLIs). Despite having adverse effects on the quality of education, plagiarism has been rapidly increasing in HLIs across the globe. Many researchers agree that the ease of access to information over the internet has made plagiarism a common occurrence in Tanzanian HLIs. In order to address this problem, many Tanzanian HLIs have enacted strict anti-plagiarism policies that require the use of software to detect plagiarism cases efficiently. Although many free and commercial plagiarism detection software exist, HLIs in Tanzania face numerous challenges in finding appropriate tools. The accuracy and effectiveness of freely available plagiarism detection software have been continuously questioned as they often provide inconsistent results. Moreover, commercial software that promise better performance have high annual subscription fees that are not easily affordable by HLIs in developing countries. This study aimed to address the need for affordable and reliable plagiarism detection tools in Tanzanian HLIs by developing an efficient algorithm for plagiarism detection. The study employed a systematic approach that involved different stakeholders in Tanzanian HLIs, including students, academic staff, and support staff. Questionnaires, unstructured interviews, and thorough literature analysis were used to identify the stakeholders’ needs and establish user requirements. The study proposed a plagiarism detection algorithm using a machine learning approach to information extraction, graph-based information retrieval, and semantic textual similarity methods. A web-based plagiarism detection system that implements the proposed algorithm was developed using open source technologies such as Symfony web framework, Neo4j graph database, MySQL database, and RabbitMQ. User Acceptance Testing (UAT) results concluded that the stakeholders positively accepted the developed algorithm. Furthermore, the developed web-based plagiarism detection system has received a Copyright Clearance Certificate from The Copyright Association of Tanzania (COSOTA), and The Committee of Vice Chancellors Principles and Provosts of Tanzania (CVCPT) has recommended the deployment of the system in Tanzanian HLIs.

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Plagiarism Detection in Marathi Language Using Semantic Analysis

Cited by 4 publications

References 11 publications

Plagiarism in the Research Reports of Indian Doctoral Students: Causes and a Remedial Action Plan

Plagiarism in the Research Reports of Indian Doctoral Students: Causes and a Remedial Action Plan

Measuring the Similarity between the Sanskrit Documents using the Context of the Corpus

Development of an Algorithm for Plagiarism Detection

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