An innovative similarity measure for sentence plagiarism detection

Giorgio Vassallo, Alfredo Cuzzocrea, Agnese Augello, Giovanni Pilato, Carmelo Spiccia

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages15
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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