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Title |
A Multi-Level Plagiarism Detection System Based on Deep Learning Algorithms |
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Author |
El Mostafa HAMBI and Faouzia Benabbou |
| Citation |
Vol. 19 No. 10 pp. 110-117 |
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Abstract |
With the high rate of online scientific publications and the accessibility to retrieval of information, has proved enormous problem of plagiarism. The techniques for detecting plagiarism are becoming increasingly advanced, whereas plagiarism of ideas still one of the greatest challenges. In this regard, some methods have been proposed in order to minimize the act of plagiarism of idea. In this paper, we propose a system of plagiarism detection of ideas based on Deep Learning Algorithms. The proposed approach deals with some problems encountered in detecting the plagiarism of ideas such as: loss of meaning or the difficulty of detection of semantic similarity between documents. Thus, our system consists of using in a first place doc2vec to have a vector representation of each sentence of a document and then we use the siamese LSTM to make learn our system that pair of documents is similar and finally we use the CNN algorithm to classify the different types of plagiarism. |
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Keywords |
Plagiarism Deep Learning Preprocessing Doc2vev neural network Long short-term memory (LSTM) Convolutional neural network (Cnn) Siamese neural network. |
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