Plagiarism Detection Based on SCAM Algorithm

Daniele Anzelmi, Domenico Carlone, Fabio Rizzello, Robert Thomsen, Dil Muhammad Akbar Hussain

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

10 Citations (Scopus)

Abstract

Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language by comparing documents. A similarity score is determined for each pair of documents which match significantly. We have implemented SCAM (Standard Copy Analysis Mechanism) which is a relative measure to detect overlap by making comparison on a set of words that are common between test document and registered document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall.
Original languageEnglish
Title of host publicationProceedings of the International MultiConference on Engineers and Computer Scientists 2011
Number of pages6
VolumeVolume I
Place of PublicationHong Kong
PublisherNewswood Limited, International Association of Engineers, IAENG
Publication date2011
Pages272-277
ISBN (Print)978-988-18210-3-4
Publication statusPublished - 2011
EventInternational MultiConference on Engineers and Computer Scientists 2011 - Hong Kong, China
Duration: 16 Mar 201118 Mar 2011

Conference

ConferenceInternational MultiConference on Engineers and Computer Scientists 2011
Country/TerritoryChina
CityHong Kong
Period16/03/201118/03/2011

Keywords

  • Plagiarism
  • SCAM
  • WordNet
  • Apache Lucene

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