Plagiarism detection using document similarity based on distributed representation

Kensuke Baba, Tetsuya Nakatoh, Toshiro Minami

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


Accurate methods are required for plagiarism detection from documents. Generally, plagiarism detection is implemented on the basis of similarity between documents. This paper evaluates the validity of using distributed representation of words for defining a document similarity. This paper proposes a plagiarism detection method based on the local maximal value of the length of the longest common subsequence (LCS) with the weight defined by a distributed representation. The proposed method and other two straightforward methods, which are based on the simple length of LCS and the local maximal value of LCS with no weight, are applied to the dataset of a plagiarism detection competition. The experimental results show that the proposed method is useful in the applications that need a strict detection of complex plagiarisms.

Original languageEnglish
Pages (from-to)382-387
Number of pages6
JournalProcedia Computer Science
Publication statusPublished - 2017
Externally publishedYes
Event8th International Conference on Advances in Information Technology, IAIT 2016 - , Macao
Duration: Dec 19 2016Dec 22 2016


  • Plagiarism detection
  • distributed representation
  • document similarity
  • longest common subsequence

ASJC Scopus subject areas

  • Computer Science(all)

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