6533b86cfe1ef96bd12c8213
RESEARCH PRODUCT
An Innovative Similarity Measure for Sentence Plagiarism Detection
Alfredo CuzzocreaAlfredo CuzzocreaGiorgio VassalloCarmelo SpicciaAgnese AugelloGiovanni Pilatosubject
business.industryComputer scienceLatent semantic analysisPlagiarism DetectionComputer Science (all)Sentence similarity measureWord error rate02 engineering and technologySimilarity measurecomputer.software_genreComplement (complexity)Theoretical Computer SciencePlagiarism detection020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPlagiarism detectionArtificial intelligenceSentence Similarity MeasurebusinesscomputerNatural language processingSentencePlagiarism detection; Sentence similarity measure; Theoretical Computer Science; Computer Science (all)description
We propose and experimentally assess Semantic Word Error Rate (SWER), an innovative similarity measure for sentence plagiarism detection. SWER introduces a complex approach based on latent semantic analysis, which is capable of outperforming the accuracy of competitor methods in plagiarism detection. We provide principles and functionalities of SWER, and we complement our analytical contribution by means of a significant preliminary experimental analysis. Derived results are promising, and confirm to use the goodness of our proposal.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |