PDLK: Plagiarism detection using linguistic knowledge

Abdi, Asad; Idris, Norisma; Alguliyev, Rasim; Alıguliyev, Ramiz M.

doi:10.1016/j.eswa.2015.07.048

Cited by 58 publications

(22 citation statements)

References 26 publications

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“…Based on the length of the passages, the algorithm automatically recognized different plagiarism forms and set the parameters for the VSM-based detection method accordingly. The "linguistic knowledge approach" proposed by Abdi et al [2] exemplifies an ensemble of detection methods. The method combines the analysis of syntactic and semantic sentence similarity using a linear combination of two similarity metrics: (i) the cosine similarity of semantic vectors and (ii) the similarity of syntactic word order vectors [2].…”

Section: Ensembles Of Detection Methodsmentioning

confidence: 99%

“…The "linguistic knowledge approach" proposed by Abdi et al [2] exemplifies an ensemble of detection methods. The method combines the analysis of syntactic and semantic sentence similarity using a linear combination of two similarity metrics: (i) the cosine similarity of semantic vectors and (ii) the similarity of syntactic word order vectors [2]. The authors showed that the method outperformed other contesters on the PAN-10 and PAN-11 corpora.…”

Section: Ensembles Of Detection Methodsmentioning

confidence: 99%

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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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Section: Ensembles Of Detection Methodsmentioning

confidence: 99%

Section: Ensembles Of Detection Methodsmentioning

confidence: 99%

Untitled

2019

CSUR

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“…One of the main gaps identified with this existing work is that it mainly focused on the structural component weighing and used Jaccard similarity metrics, where syntax or semantic traits within the document were undermined. Syntaxsemantic concept extractions using NLP techniques had shown good cogency in text-based analysis, especially in capturing intelligent manipulations (Osman, Salim, & Binwahlanc, 2012;Ram, 2014;Paul & Jamal, 2015;Abdia, Idris, Rasim Alguliyev, & Aliguliyev Ramiz, 2015;Vani & Gupta, 2017). This also includes paraphrased plagiarism detections at both the mono-lingual and cross-lingual levels (Barr on-Cedeño, Gupta, & Rosso, 2013;Franco-Salvador, Gupta, Rosso, & Banchs, 2016;Franco-Salvador, Rosso, & Montes-y-G omez, 2016).…”

Section: Related Workmentioning

confidence: 99%

Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection

Vani

Gupta

2018

Asso for Info Science & Tech

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The objective of the work is to explore the potency of integrating structural and citation information with effective syntax‐semantic text‐based analysis for scientific plagiarism detection. One of the major limitations in today's plagiarism checkers is their sole dependence on text‐based detection, where they ignore the citation and structural information. Further, the text‐based detection approaches that they employ usually fail to trace out intelligent manipulations. In the proposed work, a plagiarism detection system is presented that employs the effective coupling of various modules, namely, logical structure classifications and citation parsing, two‐stage candidate document selections, syntax‐semantic‐based exhaustive passage level analysis with plagiarism analysis using structural and citation information. Further, a new plagiarism score, namely, weighted overall similarity index is proposed, opposed to the general plagiarism scores. The proposed approach is evaluated on the data set created by Alzahrani et al. (), which contains scientific publications imposed with various plagiarism complexities. Comparison of the final system results is done against a potential baseline approach. The proposed approach exhibits considerable improvement over the comparative baseline, and hence reflects the potency of syntax‐semantic text‐based analysis with structural and citation information.

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“…Terdapat beberapa penelitian mengenai identifikasi parafrasa salah satunya adalah "PDLK: Plagiarism detection using linguistic knowledge" yang dilakukan oleh Asad Abdi, dkk [7]. Penelitian tersebut mengusulkan untuk mengenali pasangan dokumen apakah keduanya memiliki isi yang sama atau tidak.…”

Section: Literature Reviewunclassified

Identifikasi Parafrasa pada Dokumen Teks Bahasa Indonesia Menggunakan Bayesian Networks

Prayogo

2016

Indosc 2016

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PDLK: Plagiarism detection using linguistic knowledge

Cited by 58 publications

References 26 publications

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Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection

Identifikasi Parafrasa pada Dokumen Teks Bahasa Indonesia Menggunakan Bayesian Networks

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