English-Arabic Cross-language Plagiarism Detection

Al-Otaibi, Naif; Joy, Mike

doi:10.26615/978-954-452-072-4_006

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…The results showed that Sent2Vec outperformed Word2Vec and achieved a precision of 85% and a recall of 86.8%. Alotaibi et al [23] presented a CL-PD system for Arabic-English languages based on syntactic and semantic features and using different Machine Learning (ML) classifiers. The preprocessing phase included tokenization, POS tagging, removing punctuation marks, normalization, and the use of the Word2Vec and Term Frequency-Inverse Document Frequency (TF-IDF) techniques.…”

Section: Related Workmentioning

confidence: 99%

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Bouaine,

Faouzia Benabbou,

Imane Sadgali

2023

Int. J. Interact. Mob. Technol.

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Academic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the Spanish-English language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase.

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Section: Related Workmentioning

confidence: 99%

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Bouaine,

Faouzia Benabbou,

Imane Sadgali

2023

Int. J. Interact. Mob. Technol.

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“…There is still room for improvement in the second phase of plagiarism detection, according to a study of the current trends in plagiarism detection research [12,15,16]. More languages and machine learning approaches need to be explored in the field of cross-language plagiarism detection, as shown by recent studies [17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Osman

Aljahdali

2022

Mathematics

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Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

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