Algorithms and Corpora for Persian Plagiarism Detection

Asghari, Habibollah; Mohtaj, Salar; Fatemi, Omid; Faili, Heshaam; Rosso, Paolo; Potthast, Martin

doi:10.1007/978-3-319-73606-8_5

Cited by 10 publications

(12 citation statements)

References 4 publications

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“…Similarly, Oberlander and Nowson [41] focused on personality identification of bloggers. Mairesse et al [37] analysed the impact of different set of psycholinguistic features obtained with LIWC 6 and MRC, 7 showing the highest performance on openness to experience trait.…”

Section: Psychographic Profilingmentioning

confidence: 99%

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Author Profiling Tracks at FIRE

Rosso

Rangel

2020

SN COMPUT. SCI.

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Benchmarking activities are vital for fostering research and addressing new challenging problems. During the last 10 years of the FIRE initiative, we have been involved in the organization of more than ten tracks, with the aim of the creation of new resources in several languages that were made available to the research community. This allowed to compare the new several approaches on the same datasets. In this chapter, we will focus on the description of three author profiling tracks, on their data creation as well as the result analysis.

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Section: Psychographic Profilingmentioning

confidence: 99%

“…The problem of text reuse was addressed also on source code texts, both from mono-and cross-(programming) language perspectives [19,20]. The problem of plagiarism detection was addressed in 2015 and 2016 in Arabic and Persian with the aim of attract to FIRE also the research communities working with these languages [7,11].…”

Section: Pan Lab Tracks At Firementioning

confidence: 99%

Author Profiling Tracks at FIRE

Rosso

Rangel

2020

SN COMPUT. SCI.

Self Cite

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“…Following the trend of combining detection methods, we see the analysis of non-textual content features as a promising component of future integrated detection approaches. Surprisingly many papers in our collection addressed plagiarism detection for Arabic and Persian texts (e.g., References [22,118,231,262]). The interest in plagiarism detection for the Arabic language led the organizers of the PAN competitions to develop an Arabic corpus for intrinsic plagiarism detection [34].…”

Section: Extrinsic Plagiarism Detectionmentioning

confidence: 99%

“…The interest in plagiarism detection for the Arabic language led the organizers of the PAN competitions to develop an Arabic corpus for intrinsic plagiarism detection [34]. In 2015, the PAN organizers also introduced a shared task on plagiarism detection for Arabic texts [32], followed by a shared task for Persian texts one year later [22]. While these are promising steps toward improving plagiarism detection for Arabic, Wali et al [262] noted that the availability of corpora and lexicons for Arabic is still insufficient when compared to other languages.…”

Section: Extrinsic Plagiarism Detectionmentioning

confidence: 99%

Untitled

2019

CSUR

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This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Specialized information retrieval; • Computing methodologies → Natural language processing; Machine learning approaches; • Applied computing → Digital libraries and archives;

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“…Promising are also results of studies of cross-language text-borrowing by non-native English speakers, who may copy, translate to English, and edit texts published in their mother tongue ( 59 60 ). Intelligent systems are mainly designed to recognize incorrect processing of German, French, and other Roman texts, though few recent studies have also focused on other languages ( 61 ). One of the proposed principles of unmasking cross-language plagiarism is based on analysing keywords in suspicious texts ( 62 ).…”

Section: Detection Of Plagiarismmentioning

confidence: 99%

Plagiarism in the Context of Education and Evolving Detection Strategies

et al. 2017

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Plagiarism may take place in any scientific journals despite currently employed anti-plagiarism tools. The absence of widely acceptable definitions of research misconduct and reliance solely on similarity checks do not allow journal editors to prevent most complex cases of recycling of scientific information and wasteful, or ‘predatory,’ publishing. This article analyses Scopus-based publication activity and evidence on poor writing, lack of related training, emerging anti-plagiarism strategies, and new forms of massive wasting of resources by publishing largely recycled items, which evade the ‘red flags’ of similarity checks.In some non-Anglophone countries ‘copy-and-paste’ writing still plagues pre- and postgraduate education. Poor research management, absence of courses on publication ethics, and limited access to quality sources confound plagiarism as a cross-cultural and multidisciplinary phenomenon. Over the past decade, the advent of anti-plagiarism software checks has helped uncover elementary forms of textual recycling across journals. But such a tool alone proves inefficient for preventing complex forms of plagiarism. Recent mass retractions of plagiarized articles by reputable open-access journals point to critical deficiencies of current anti-plagiarism software that do not recognize manipulative paraphrasing and editing. Manipulative editing also finds its way to predatory journals, ignoring the adherence to publication ethics and accommodating nonsense plagiarized items. The evolving preventive strategies are increasingly relying on intelligent (semantic) digital technologies, comprehensively evaluating texts, keywords, graphics, and reference lists. It is the right time to enforce adherence to global editorial guidance and implement a comprehensive anti-plagiarism strategy by helping all stakeholders of scholarly communication.

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Algorithms and Corpora for Persian Plagiarism Detection

Cited by 10 publications

References 4 publications

Author Profiling Tracks at FIRE

Author Profiling Tracks at FIRE

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Plagiarism in the Context of Education and Evolving Detection Strategies

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