Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

Roostaee, Meysam; Fakhrahmad, Seyed Mostafa; Sadreddini, Mohammad Hadi

doi:10.1016/j.eswa.2020.113718

Cited by 21 publications

(9 citation statements)

References 47 publications

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“…Meysam et al [5] suggested a two-level matching strategy to properly align similarity fragments from the source and suspicious documents. It is taken into consideration both syntactic and semantic components.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome the phenomenon of text plagiarism, researchers have developed several approaches that rely on text linguistic analysis, which depends on its semantic features to calculate the similarity. Some approaches are established on n-grams features [2,3], while the others are established on converting the texts to vectors using Vector Space Model (VSM) representation [4,5], Wordnet basedknowledge [6,7], several pre-trained methods such as Word2Vec [8], InferSent [9], Glove [10], etc. These vectors are used as an input for cosine, Dice, Jaccard, Fuzzy, Match, or Euclidean resemblance measurements.…”

Section: Introductionmentioning

confidence: 99%

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Reliable plagiarism detection system based on deep learning approaches

El-Rashidy

Mohamed

El-Fishawy

et al. 2022

Neural Comput & Applic

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The phenomenon of scientific burglary has seen a significant increase recently due to the technological development in software. Therefore, many types of research have been developed to address this phenomenon. However, detecting lexical, syntactic, and semantic text plagiarism remains to be a challenge. Thus, in this study, we have computed and recorded all the features that reflect different types of text similarities in a new database. The created database is proposed for intelligent learning to solve text plagiarism detection problems. Using the created database, a reliable plagiarism detection system is also proposed, which depends on intelligent deep learning. Different approaches to deep learning, such as convolution and recurrent neural network architectures, were considered during the construction of this system. A comparative study was implemented to evaluate the proposed intelligent system on the two benchmark datasets: PAN 2013 and PAN 2014 of the PAN Workshop series. The experimental results showed that the proposed system based on long short-term memory (LSTM) achieved the first rank compared to up-to-date ranking systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliable plagiarism detection system based on deep learning approaches

El-Rashidy

Mohamed

El-Fishawy

et al. 2022

Neural Comput & Applic

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“…Textual plagiarism manifests in various forms which can be detected by advanced search engines and software that analyze similarities across a sizeable amount of digitized publications. Direct, or word-for-word, and Reumatologia 2021; 59/3 translational forms of plagiarism are relatively easy to detect by employing search engines and anti-plagiarism software [24,25]. Paraphrasing and patchwork plagiarism may confuse plagiarism detection systems by plagiarists' attempts to replace a few words in the original text and misappropriate phrases and sentences.…”

Section: Instances Of Plagiarismmentioning

confidence: 99%

Plagiarism detection and prevention: a primer for researchers

Zimba¹,

Gasparyan²

2021

Reumatologia

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Plagiarism is an ethical misconduct affecting the quality, readability, and trustworthiness of scholarly publications. Improving researcher awareness of plagiarism of words, ideas, and graphics is essential for avoiding unacceptable writing practices. Global editorial associations have publicized their statements on strategies to clean literature from redundant, stolen, and misleading information. Consulting related documents is advisable for upgrading author instructions and warning plagiarists of academic and other consequences of the unethical conduct. A lack of creative thinking and poor academic English skills are believed to compound most instances of redundant and "copy-and-paste" writing. Plagiarism detection software largely relies on reporting text similarities. However, manual checks are required to reveal inappropriate referencing, copyright violations, and substandard English writing. Medical researchers and authors may improve their writing skills and avoid the same errors by consulting the list of retractions due to plagiarism which are tracked on the PubMed platform and discussed on the Retraction Watch blog.

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“…This study indicates Nazief-Adriani performs superior to the stemmer porter. Roostaee et al [15] proposed a two-level matching scheme for plagiarism detection. They used a weighted multilingual word embeddings and graph-of-words representation of text to understand the relationship between words in a sentence.…”

Section: Introductionmentioning

confidence: 99%

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Ahnaf

Hasan

Sworna

et al. 2023

IJECE

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Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31%

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Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

Cited by 21 publications

References 47 publications

Reliable plagiarism detection system based on deep learning approaches

Reliable plagiarism detection system based on deep learning approaches

Plagiarism detection and prevention: a primer for researchers

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

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