PPChecker: plagiarism pattern checker in document copy detection
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
A coarse-to-fine framework to efficiently thwart plagiarism
Pattern Recognition
A new approach for cross-language plagiarism analysis
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
Automatic detection of local reuse
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Towards document plagiarism detection based on the relevance and fragmentation of the reused text
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
SimPaD: A word-similarity sentence-based plagiarism detection tool on Web documents
Web Intelligence and Agent Systems
Plagiarism detection based on structural information
Proceedings of the 20th ACM international conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
Online plagiarism detection through exploiting lexical, syntactic, and semantic information
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Determining and characterizing the reused text for plagiarism detection
Expert Systems with Applications: An International Journal
Technical Section: EXOD: A tool for building and exploring a large graph of open datasets
Computers and Graphics
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When automatic plagiarism detection is carried out considering a reference corpus, a suspicious text is compared to a set of original documents in order to relate the plagiarised text fragments to their potential source. One of the biggest difficulties in this task is to locate plagiarised fragments that have been modified (by rewording, insertion or deletion, for example) from the source text. The definition of proper text chunks as comparison units of the suspicious and original texts is crucial for the success of this kind of applications. Our experiments with the METER corpus show that the best results are obtained when considering low level word n -grams comparisons (n = {2,3}).