On the use of character n-grams as the only intrinsic evidence of plagiarism

Bensalem, Imene; Rosso, Paolo; Chikhi, Salim

doi:10.1007/s10579-019-09444-w

Cited by 7 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the 𝑛 − 𝑔𝑟𝑎𝑚 profile of a given text is defined as the set containing all the 𝑛 − 𝑔𝑟𝑎𝑚𝑠 of a predetermined length, 𝑛, beside their frequency. The work in [13] summarizes the approaches for detecting intrinsic plagiarism where character 𝑛 − 𝑔𝑟𝑎𝑚𝑠 are used.…”

Section: Related Workmentioning

confidence: 99%

Mining Deviations in Document Writing Style through Vector Dissimilarity

Kadhim

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

Doubts arise about the originality of a document when noticing a change in its writing style. This evidence to plagiarism has made the intrinsic approach for detecting plagiarism uncover the plagiarized passages through the analysis of the writing style for the suspicious document where a reference corpus to compare with is absent. The proposed work aims at discovering the deviations in document writing style through applying several steps: Firstly, the entire document is segmented into disjointed segments wherein each corresponds to a paragraph in the original document. For the entire document and for each segment, center vectors comprising average weight of their word are constructed. Second, the degree of closeness is calculated through applying Cosine similarity to measure for each segment, the deviation of its center vector from the center vector of the entire document. Additionally, word n-gram length will be investigated to show its effect on the proposed system performance wherein, center vectors are computed considering word n-grams for different values of n (n= 1, 2, and 3). Performance evaluation of the proposed method was accomplished through the use of Precision, Recall, F-measure, Granularity, and Plagdet as evaluation measures. Moreover, PAN-PC-09 and PAN-PC-11 were used for detecting intrinsic plagiarism as evaluation corpora. It is shown that the proposed approach has achieved results that are comparable to the state-of-the-art methods. Positive impact was observed through discovering deviations in document writing style by computing weight vectors dissimilarity rather than calculating the difference between the word n-grams that exist in segments and their corresponding word n-grams in the suspicious document. Furthermore, when considering the length of word n-gram, better results were recorded for system performance when word bi-grams was used compared to word uni-grams and word tri-grams.

show abstract

Section: Related Workmentioning

confidence: 99%

Mining Deviations in Document Writing Style through Vector Dissimilarity

Kadhim

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…The set of all the 𝑛 − 𝑔𝑟𝑎𝑚𝑠 of a predefined length, 𝑛, extracted along with their frequencies from a given text, is referred to as the text's 𝑛 − 𝑔𝑟𝑎𝑚 profile. Methods for intrinsic plagiarism detection; wherein character 𝑛 − 𝑔𝑟𝑎𝑚𝑠 are used, were summarized in [13].…”

Section: Related Workmentioning

confidence: 99%

An Improved Outlier Detection Model for Detecting Intrinsic Plagiarism

Kadhim

khalaf²

2022

Iraqi Journal of Science

View full text Add to dashboard Cite

In the task of detecting intrinsic plagiarism, the cases where reference corpus is absent are to be dealt with. This task is entirely based on inconsistencies within a given document. Detection of internal plagiarism has been considered as a classification problem. It can be estimated through taking into consideration self-based information from a given document. The core contribution of the work proposed in this paper is associated with the document representation. Wherein, the document, also, the disjoint segments generated from it, have been represented as weight vectors demonstrating their main content. Where, for each element in these vectors, its average weight has been considered instead of its frequency. The proposed work has been evaluated in terms of Precision, Recall, F-measure, Granularity, and Plagdet. It is shown that the attained results are comparable to the ones attained by the best state-of-the-art methods. Where, through applying the proposed method to PAN-PC-09 and PAN-PC-11 for the detection of intrinsic plagiarism, a Recall scores of 0.4503 and 0.4303 have been recorded, even though further improvement for Precision (0.3308 and 0.2806) and Granularity (1.1765 and 1.1111) needs to be made. Concerning f-measure, the proposed approach has recorded 0.3814 and 0.3397. In terms of the total performance of a plagiarism detection approach, Plagdet, the proposed method has recorded 0.3399 and 0.3151.

show abstract

“…In this section we provide a brief overview of various approaches proposed for the detection of plagiarism and paraphrase plagiarism. In particular, approaches based on character and word n-gram similarity (Bensalem et al 2019;Sánchez-Vega et al 2017), vector space models (Sanchez-Perez et al 2014), natural language processing (Chong 2013;Kanjirangat and Gupta 2018) machine translation similarity metrics (Madnani et al 2012) and alignment algorithms (Nichols et al 2019) have been successfully applied towards plagiarism detection. Despite these advances, plagiarism detection when text has been paraphrased remains a challenge due to limited success in measuring semantic overlap (Carmona et al 2018).…”

Section: Plagiarism Detectionmentioning

confidence: 99%

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Alvi

Stevenson

Clough

2021

Int J Educ Technol High Educ

View full text Add to dashboard Cite

Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of $$\hbox {F}_1$$ F 1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection.

show abstract

On the use of character n-grams as the only intrinsic evidence of plagiarism

Cited by 7 publications

References 30 publications

Mining Deviations in Document Writing Style through Vector Dissimilarity

Mining Deviations in Document Writing Style through Vector Dissimilarity

An Improved Outlier Detection Model for Detecting Intrinsic Plagiarism

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Contact Info

Product

Resources

About