Disguised plagiarism detection in Arabic text documents

Nagoudi, El Moatez Billah; Cherroun, Hadda; Alshehri, Ali

doi:10.1109/icnlsp.2018.8374395

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…− The feature matrix for the passage-phase After extracting and creating three feature vectors psim(U,V), pinter(U,V), and pimp(U,V), we combine them into a two-dimensional matrix of size (n+m) x 3 where n+m is the total number of passages from suspicious and source documents. The feature matrix for all passages in the pair of suspicious and source documents is determined as in (10). It is used as the input for the multi-layer LSTM network model, described in section 2.2.2.…”

Section: Maximize Passage Intersectionmentioning

confidence: 99%

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A two-phase plagiarism detection system based on multi-layer long short-term memory networks

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2021

IJ-AI

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Finding plagiarism strings between two given documents are the main task of the plagiarism detection problem. Traditional approaches based on string matching are not very useful in cases of similar semantic plagiarism. Deep learning approaches solve this problem by measuring the semantic similarity between pairs of sentences. However, these approaches still face the following challenging points. First, it is impossible to solve cases where only part of a sentence belongs to a plagiarism passage. Second, measuring the sentential similarity without considering the context of surrounding sentences leads to decreasing in accuracy. To solve the above problems, this paper proposes a two-phase plagiarism detection system based on multi-layer long short-term memory network model and feature extraction technique: (i) a passage-phase to recognize plagiarism passages, and (ii) a word-phase to determine the exact plagiarism strings. Our experiment results on PAN 2014 corpus reached 94.26% F-measure, higher than existing research in this field.

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Section: Maximize Passage Intersectionmentioning

confidence: 99%

“…Cherroun et al [10] proposed a two-phase system using a supervised learning approach to detect plagiarism in Arabic. The first phase produced a representing vector for each sentence by combining different features, including word embedding, word alignment, term frequency weighting, and part-of-speech tagging.…”

Section: Introductionmentioning

confidence: 99%