Plagiarism Detection Based on a Novel Trie-Based Approach

Talebpour, Alireza; Laskoukelayeh, Mohammad Shirzadi; Aminolroaya, Zahra

doi:10.1007/978-3-319-73606-8_8

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…For example, they considered a candidate seed as a summary case if the source passage is more than 3 times longer than the suspicious passage. For Persian text alignment task, the source document is indexed into the trie structure in (Talebpour at al., 2016). Then the suspicious document is analyzed word by word against trie structure to find potential sources.…”

Section: Source and Suspicious Documentsmentioning

confidence: 99%

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

Gharavi

Veisi

Rosso

2019

Neural Comput & Applic

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The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different type of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.

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Section: Source and Suspicious Documentsmentioning

confidence: 99%

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

Gharavi

Veisi

Rosso

2019

Neural Comput & Applic

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“…Author [24] proposed a novel technique to detect suspicious text documents over original documents. Their dataset is PersianPlagDet text documents then data preparation is done step by step such as tokenization, POS tagging, cleansing and finding nouns and synsets through Farsnet after it they used trie tree data structure which enhance quick searching and insertion.…”

Section: Related Workmentioning

confidence: 99%

A Review on Various Plagiarism Detection Systems Based on Exterior and Interior Method

Khan¹,

Agrawal²,

Ansari³

2018

International Journal of Advanced Research in Computer and Comm

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Plagiarism is an intellectual theft. It means representation of author's output as self-contribution without mentioning reference or attribution. Plagiarism is considered as a fraudulent act which simply means forgery of someone's fresh content by not paying any tribute. It is proliferated due to increasing access to multiple resources and simply using copy paste method. With the immense growth of internet resources new technologies and original ideas or innovative thoughts are available effortlessly due to which protection over such intellectual property is challenging, but important. Therefore, in this paper we delineate about plagiarism and then classification of plagiarism. We also discuss two main approaches-Exterior and Interior methods for plagiarism detection with their characteristics methods based on their grammar styles, lexical, syntax or Stylometric features. Further, literature analysis of various algorithms is described to find out their issues and challenges improvised scope of work in future.

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“…Finally, they revise the current research situations and derive research methods to be followed further. In this paper [18], author proposed a comparison between source and suspected text documents by using some text documents as a case study. They made the data ready by preprocessing, tokenization and Morphological analysis before documents comparison.…”

Section: Related Workmentioning

confidence: 99%

“…Here in table 1 below is the complete comparison of available work for the plagiarism detection taken from different references such as [15], [16], [17], [18], [19],20], [21]. Table 1.…”

Section: Related Workmentioning

confidence: 99%

A Survey: Document Spamming Detection and Plagiarism Content Techniques

Ruthia¹,

Tiwary²

2018

International Journal of Advanced Research in Computer and Comm

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Plagiarism is defined as representing someone else words, thoughts, knowledge, methods, programs etc in our own name. Plagiarism has a wider meaning by paraphrasing someone else text by replacing some data or method in our way is also a plagiarism. It also violates the rule if you don"t mention someone author name when you are copying their data in your own way of representation. The detection techniques are applied by differentiating between variety of languages such as natural and programming language. From the existence of the previous approaches like plagiarism detection technique and SCAM analysis from the document stream. A further solution to find plagiarism in the given input data, and textual data is required. Here we are using QAP based Function minimization approach. This technique can be used to find document from the stream. Further QAP could enhance the required minimization function.

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Plagiarism Detection Based on a Novel Trie-Based Approach

Cited by 3 publications

References 8 publications

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

A Review on Various Plagiarism Detection Systems Based on Exterior and Interior Method

A Survey: Document Spamming Detection and Plagiarism Content Techniques

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