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Outline

Title
Abstract
Background and Related Work
Scope and Approach
Related Work 2.1. Webshiled
Web Content Analysis Technology
Statistical Phase
Results of Experiment
Hough Transform-Based Methods
Comparative Analysis
Hci-Related Terminology and Concepts
Dataset Used for Analysis
Previous Work
Result and Discussion
Cluster-Based Summary Generation
Experimental Procedure
Discussion
Data Recovery Challenges
Recovery Objectives
Data Explosion
Nature of Data
Levels of Data Protection
Data Policy Management
Metadata Search Engine Capability
Summary
Case Study
Background and Motivation
Literature Review and Related Works
Proposed Methods
Experimental Configuration
Experimental Results
Related Works
Datasets and Method
Evaluation and Results
Research Methodolgy
Results and Discussion
Conclusion
Conclusions and Recommendations
Overview
Results and Limitations
Introduction
Related Work
Overview of Topic Model
Conclusions
Discussion and Threats to Validity
Limitations of Similarity Threshold
Limitations of Clone Detector
References
All Topics
Computer Science
Natural Language Processing

A Framework for Plagiarism Detection in Arabic Documents

Profile image of Imtiaz KhanImtiaz Khan

2015, Computer Science & Information Technology ( CS & IT )

https://doi.org/10.5121/CSIT.2015.50201
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212 pages

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Abstract

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.

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Imene Bensalem

2020

This thesis deals with two major topics: plagiarism detection in Arabic documents, and plagiarism detection based on the writing style changes in the suspicious document, which is called intrinsic plagiarism detection. This approach is an alternative to the text-matching approach, notably, in the absence of the plagiarism source. Our key contributions in these two areas lie first, in the development of Arabic corpora to allow for the evaluation of plagiarism detection software on this language and, second, in the development of a language-independent intrinsic plagiarism detection method that exploits the character n-grams in a machine learning approach while avoiding the curse of dimensionality. Our third key contribution is an investigation on which character n-grams, in terms of their frequency and length, are the best to detect plagiarism intrinsically. We carried out our experiments on standardised English corpora and also on the developed Arabic corpora using the method we developed and one of the most prominent intrinsic plagiarism detection methods. The findings of our analysis can be exploited by the future intrinsic plagiarism detection methods that use character n-grams. In addition to the above-mentioned technical contributions, we provide the reader with comprehensive and critical surveys of the literature of Arabic plagiarism detection and intrinsic plagiarism detection, which were lacking in both topics.

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Query Optimization in Arabic Plagiarism Detection: An Empirical Study
Imtiaz Khan

International Journal of Intelligent Systems and Applications, 2014

This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and fmeasure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.

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Related topics

  • Plagiarism Detection
  • Arabic NLP
  • Similarity Computation
  • Query Generation
  • Document Retrieval
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