Building Arabic corpora from Wikisource

Bensalem, Imene; Chikhi, Salim; Rosso, Paolo

doi:10.1109/aiccsa.2013.6616474

Cited by 5 publications

(3 citation statements)

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“…We used specifically the test part of each corpus 10 . The Arabic corpus (InAra) (Bensalem et al 2013a(Bensalem et al , 2013b has been built by ourselves, following PAN annotation standards, and has been used in AraPlagDet 2015 11 , the first plagiarism detection competition on Arabic documents (Bensalem et al 2015).…”

Section: Datasets and Performance Measuresmentioning

confidence: 99%

On the use of character n-grams as the only intrinsic evidence of plagiarism

Bensalem

Rosso

Chikhi

2019

Lang Resources & Evaluation

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When a shift in writing style is noticed in a document, doubts arise about its originality. Based on this clue to plagiarism, the intrinsic approach to plagiarism detection identifies the stolen passages by analysing the writing style of the suspicious document without comparing it to textual resources that may serve as sources for the plagiarist. Character n-grams are recognised as a successful approach to modelling text for writing style analysis. Although prior studies have investigated the best practice of using character n-grams in authorship attribution and other problems, there is still a need for such investigations in the context of intrinsic plagiarism detection. Moreover, it has been assumed in previous works that the ways of using character ngrams in authorship attribution remain the same for intrinsic plagiarism detection. In this paper, we study the effect of character n-grams frequency and length on the performance of intrinsic plagiarism detection. Our experiments utilise two state-ofthe-art methods and five large document collections of PAN labs written in English and Arabic. We demonstrate empirically that the low-and the high-frequency ngrams are not equally relevant for intrinsic plagiarism detection, but their performance depends on the way they are exploited. Keywords Intrinsic plagiarism detection . Character n-grams . Stylistic features . Writing style analysisWe are very grateful to the anonymous reviewers for their insightful suggestions and constructive comments that greatly improved the paper.

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Section: Datasets and Performance Measuresmentioning

confidence: 99%

On the use of character n-grams as the only intrinsic evidence of plagiarism

Bensalem

Rosso

Chikhi

2019

Lang Resources & Evaluation

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“…Commonly applied syntax analysis tools include Penn Treebank, 3 Citar, 4 TreeTagger, 5 and Stanford parser. 6 Several papers present resources for Arabic [33,34,227] and Urdu [54] language processing.…”

Section: Preprocessingmentioning

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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“…We finally decided to build our corpus from Arabic Wikisource which is a library of heritage books and public domain texts. Furthermore, most of its documents are tagged with topics and author names (see our paper [19] for further details on the text compilation from Wikisource). We also added some texts from other sources, after making sure that they are without copyright.…”

Section: C1mentioning

confidence: 99%