Abstract
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 language | English |
|---|---|
| Pages (from-to) | 382-387 |
| Number of pages | 6 |
| Journal | Procedia Computer Science |
| Volume | 111 |
| DOIs | |
| Publication status | Published - Jan 1 2017 |
| Event | 8th International Conference on Advances in Information Technology, IAIT 2016 - , Macao Duration: Dec 19 2016 → Dec 22 2016 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)
Cite this
Plagiarism detection using document similarity based on distributed representation. / Baba, Kensuke; Nakatoh, Tetsuya; Minami, Toshiro.
In: Procedia Computer Science, Vol. 111, 01.01.2017, p. 382-387.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Plagiarism detection using document similarity based on distributed representation
AU - Baba, Kensuke
AU - Nakatoh, Tetsuya
AU - Minami, Toshiro
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029367850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029367850&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2017.06.038
DO - 10.1016/j.procs.2017.06.038
M3 - Conference article
AN - SCOPUS:85029367850
VL - 111
SP - 382
EP - 387
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -