Automatic Detection of Local Reuse

Mittelbach, Arno; Lehmann, Lasse; Rensing, Christoph; Steinmetz, Ralf

doi:10.1007/978-3-642-16020-2_16

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…If the rewriting process involves complex editing operations (e.g., lexical substitution, changes in syntax, summarization, synonym replacement, altering word order, or verb or noun nominalization) then the borrowed text transforms into an independently written piece (Clough, Gaizauskas, Piao, & Wilks, ; Maurer, Kappe, & Zaka, ). Moreover, new text can be created using text from one or more sources and the amount of reused text varies from local text reuse (such as, a single word, small chunks, or sentences) to global text reuse (i.e., an entire document; Mittelbach, Lehmann, Rensing, & Steinmetz, ; Seo & Croft, ).…”

Section: Introductionmentioning

confidence: 99%

CLEU ‐ A Cross‐language english‐urdu corpus and benchmark for text reuse experiments

Muneer¹,

Sharjeel

Iqbal

et al. 2018

Asso for Info Science & Tech

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Text reuse is becoming a serious issue in many fields and research shows that it is much harder to detect when it occurs across languages. The recent rise in multi-lingual content on the Web has increased cross-language text reuse to an unprecedented scale. Although researchers have proposed methods to detect it, one major drawback is the unavailability of large-scale gold standard evaluation resources built on real cases. To overcome this problem, we propose a cross-language sentence/passage level text reuse corpus for the English-Urdu language pair. The Cross-Language English-Urdu Corpus (CLEU) has source text in English whereas the derived text is in Urdu. It contains in total 3,235 sentence/passage pairs manually tagged into three categories that is near copy, paraphrased copy, and independently written. Further, as a second contribution, we evaluate the Translation plus Mono-lingual Analysis method using three sets of experiments on the proposed dataset to highlight its usefulness. Evaluation results (f 1 =0.732 binary, f 1 =0.552 ternary classification) indicate that it is harder to detect cross-language real cases of text reuse, especially when the language pairs have unrelated scripts. The corpus is a useful benchmark resource for the future development and assessment of cross-language text reuse detection systems for the English-Urdu language pair.

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Section: Introductionmentioning

confidence: 99%

CLEU ‐ A Cross‐language english‐urdu corpus and benchmark for text reuse experiments

Muneer¹,

Sharjeel

Iqbal

et al. 2018

Asso for Info Science & Tech

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“…In this unit, we present a variety of techniques proposed for detection of plagiarism in information represented as text (see, e.g., [24,16,32,13,2,25]), images and video (see, e.g., [14,15,23,22,19,21,18]). At the heart of these techniques are scalable algorithms for similarity detection, and we identify common techniques explored across the different types of information.…”

Section: Copy Detection For Unstructured Datamentioning

confidence: 99%

“…The Information Retrieval community has devoted considerable effort to finding plagiarism, near-duplicate web pages and text reuse (see, e.g., [24,16,32,13,2,25]). The Multimedia community has considered techniques for copy detection of images and video, especially in the presence of distortion (see, e.g., [14,15,23,22,19,21,18]).…”

Section: Introductionmentioning

confidence: 99%

Large-scale copy detection

Dong

Srivastava

2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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The Web has enabled the availability of a vast amount of useful information in recent years. However, the web technologies that have enabled sources to share their information have also made it easy for sources to copy from each other and often publish without proper attribution. Understanding the copying relationships between sources has many benefits, including helping data providers protect their own rights, improving various aspects of data integration, and facilitating in-depth analysis of information flow.The importance of copy detection has led to a substantial amount of research in many disciplines of Computer Science, based on the type of information considered, such as text, images, videos, software code, and structured data. This tutorial explores the similarities and differences between the techniques proposed for copy detection across the different types of information. We also examine the computational challenges associated with large-scale copy detection, indicating how they could be detected efficiently, and identify a range of open problems for the community.

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“…Many kinds of text reuse detection techniques have been proposed from time to time by different authors, including: ; Gustafson et al (2008); Mittelbach et al (2010);Palkovskii, Muzyka, and Belov (2010); Seo and Croft (2008);Clough et al (2002); Gupta and Rosso (2012); Bar, Zesch, and Gurevych (2012);and Potthast et al (2013a, b). Gipp et al (2013) proposed Citation-based Plagiarism Detection.…”

Section: Related Workmentioning

confidence: 99%