Plagiarism Detection using Semantic Analysis

Al-Shamery, Eman S.; Gheni, Hadeel Qasem

doi:10.17485/ijst/2016/v9i1/84235

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…Shenoy et al invented an algorithm capable of learning ontology from documents using the Web Ontology Language OWL and then applying it to ontology mapping for an automatic system for semantic plagiarism detection (Shenoy et al, 2012). Al‐Shamery and Gheni consider finding synonyms over WordNet in the same place as proof of semantic plagiarism (Al‐Shamery & Gheni, 2016). Alzahrani and Salim used a fuzzy semantic‐based string similarity approach for their plagiarism detection method (Alzahrani & Salim, 2010).…”

Section: Related Workmentioning

confidence: 99%

Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Vrbanec

Meštrović

2023

Expert Systems

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Automatic detection of concealed plagiarism in the form of paraphrases is a difficult task, and finding a successful unsupervised approach for paraphrase detection is necessary as a precondition to change that. This comparative study identified the most efficient methods for unsupervised paraphrased document detection using similarity measures alone or combined with Deep Learning (DL) models. It proved the hypothesis that some DL models are more successful than the best statistically-based methods in that task. Many experiments were carried out, and their results were compared. The text similarities between documents are obtained from 60 different methods using five paraphrase corpora, including the new one made by authors, as an important original contribution. Some DL models achieved significantly better results than those obtained by the best statistical methods, especially pre-trained transformer-based language models with average values of Accuracy and F1 of 85.8% and 88.3%, respectively, with top values of 99.9% and 98.4% for Accuracy and F1 on some corpora. These results are even better than those of supervised and combined approaches. Therefore, here presented results prove that detecting concealed plagiarism becomes an attainable goal. This study highlighted those language models with the best overall results for paraphrase detection as best suited for further research. The study also discussed the choice of similarity/distance measure paired with embeddings produced by DL models and some advantages of using cosine similarity as the fastest measure. For 60 different methods, complexity has been defined in O notation. Times needed for their implementation have also been presented. The article's results and conclusions are a firm base for future semantic similarity, paraphrasing, and plagiarism detection studies, clearly marking state-ofthe-art tools and methods.

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Section: Related Workmentioning

confidence: 99%

Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Vrbanec

Meštrović

2023

Expert Systems

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“…With an increase in cases of academic plagiarism, re-searchers have come up with different approaches to detect the same. E. Al-Shamery and H. Gheni in [2] described a system to check plagiarism on documents based on Computer Science related elds. Documents were processed only if they belonged to these elds else were rejected.…”

Section: Related Workmentioning

confidence: 99%

Semantic Similarity and Plagiarism Checker

Vyas,

Kumar,

Banerjee

et al. 2023

Preprint

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Today all educational institutes stress on the importance of continuous evaluation. This directly impacts the number of assessments to be done for various projects, quizzes, assignments, and exams. It becomes a time and resource- intensive task for the instructors to evaluate these submissions with accuracy. Additionally, the instructor also needs to keep a check on whether any student has plagiarized from the web or if there is any case of intrinsic plagiarism among the students. With a huge set of information on the internet, one can easily get away with plagiarism. There are tools available for such requirements, but almost all of them are paid, and moreover, instructors need to run them one by one, which is a difficult task in itself. Thus, we developed a one-stop solution for the entire problem, which, as per our knowledge, is not in place. We have developed a single tool that can measure semantic plagiarism (based on meaning), text entailment, online plagiarism, and real plagiarism as well. In the future, work can be aligned in the direction of semantics of research papers as well to grade answers.

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“…WordNet has been used by more than a few researchers. Al-Shamery and Gheni (2016) [22] consider that finding synonyms (over WordNet) on the same place in comparing documents is to be the proof of semantic plagiarism.…”

Section: Related Workmentioning

confidence: 99%

The struggle with academic plagiarism: Approaches based on semantic similarity

Vrbanec

Meštrović

2017

2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is -is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.

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Plagiarism Detection using Semantic Analysis

Cited by 19 publications

References 4 publications

Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Semantic Similarity and Plagiarism Checker

The struggle with academic plagiarism: Approaches based on semantic similarity

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