Text Similarity Based on Data Compression in Arabic

Soori, Hussein; Prilepok, Michal; Platoš, Jan; Berhan, Eshetie; Snášel, Václav

doi:10.1007/978-3-642-41968-3_22

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…In (Soori et al, 2014), a plagiarism detection method using text similarity for Arabic language text with 150 documents and 330 paragraphs have been proposed. The findings of the study show that the similarity measurement based on LZW (Lempel-Ziv-Welch) (Ziv & Lempel, 1977) comparison algorithms are very efficient for the plagiarized part of the Arabic text documents with a successful rate of 71.42%.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Segmentation Prototype for Arabic Text-Based Documents

Alouane-Ksouri

Hidri

2015

International Journal of Service Science, Management, Engineering, and Technology

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The contribution of this work relates to the field of Arabic text-based document analysis for the detection of plagiarism. This analysis will be carried out according to the triadic computation model of document similarity. The authors propose a hybrid segmentation prototype for Arabic text-based documents that links different processing steps in order to generate the similarity rate between the documents of an Arabic corpus. It involves two segmentation systems and a morphological analysis in order to obtain a matrix representation adapted to the triadic similarity computation according to three abstraction levels: documents, sentences and words.

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Section: Discussionmentioning

confidence: 99%

Hybrid Segmentation Prototype for Arabic Text-Based Documents

Alouane-Ksouri

Hidri

2015

International Journal of Service Science, Management, Engineering, and Technology

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“…[21] N-gram similarity techniques that are used give a score of more than 80% accuracy. In one of the studies [22], 71.42% was detected as plagiarism. In addition, the percentage of partially stolen articles or documents was 28.85%.…”

Section: A Lexical-based Similaritymentioning

confidence: 96%

Arabic Language Documents’ Similarity and its Challenges (A Review)

Saleh¹,

Fadzli²,

El-Ebiary³

2020

IJETT

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Measuring the similarity of documents is of great importance and has influences on many areas and subjects such as Information Retrieval (IR), redrafting discovered, classified documents, and conversational agent programs. Recently, several scientific studies have been interested in implementing the similarity of documents in Arabic language applications. In this study, we review most of the techniques used in measuring the similarity between documents in Arabic; these techniques are classified into three types, mainly lexical, semantic, and finally hybrid. Hence, most of the techniques and methods adopted to measure the similarity between documents were reviewed to determine the most appropriate ones to measure the similarity in Arabic. However, the measurement of the percentage of similarity in documents in the Arabic language represents a major challenge faced by most of the researchers due to the nature of the Arabic language, such as complex morphological processes, ambiguity, and a general lack of resources.

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“…Following the trend of combining detection methods, we see the analysis of non-textual content features as a promising component of future integrated detection approaches. Surprisingly many papers in our collection addressed plagiarism detection for Arabic and Persian texts (e.g., References [22,118,231,262]). The interest in plagiarism detection for the Arabic language led the organizers of the PAN competitions to develop an Arabic corpus for intrinsic plagiarism detection [34].…”

Section: Extrinsic Plagiarism Detectionmentioning

confidence: 99%

Untitled

2019

CSUR

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This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Specialized information retrieval; • Computing methodologies → Natural language processing; Machine learning approaches; • Applied computing → Digital libraries and archives;

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Text Similarity Based on Data Compression in Arabic

Cited by 10 publications

References 9 publications

Hybrid Segmentation Prototype for Arabic Text-Based Documents

Hybrid Segmentation Prototype for Arabic Text-Based Documents

Arabic Language Documents’ Similarity and its Challenges (A Review)

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