An integrated approach for intrinsic plagiarism detection

Alsallal, Muna; Iqbal, Rahat; Palade, Vasile; Amin, Saad; Chang, Victor

doi:10.1016/j.future.2017.11.023

Cited by 31 publications

(16 citation statements)

References 32 publications

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“…AlexNet opened a milestone for deep learning in computer vision, and since then, ILSVRC has been continuously topped by deep learning every year. The literature [7] proposed the VGG model, still consisting of convolutional and fully connected layers, which has a very consistent network structure, using all 3 × 3 convolutions and 2 × 2 pooling from start to finish; however, it suffers from consuming more computational resources and using more parameters, resulting in more memory usage. Later, the literature [8,9] investigated deeper network structures based on AlexNet, which is no longer limited to the structure of convolutional and fully connected layers and used mean pooling layers instead of fully connected layers for classification and proposed the GoogleNet model, which greatly reduces the number of model parameters and has a top 5 error rate of only 6.7%.…”

Section: Related Workmentioning

confidence: 99%

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Zhang

2021

Wireless Communications and Mobile Computing

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Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high-level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning-based end-to-end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource-poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end-to-end neural network machine translation models under low-resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.

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Section: Related Workmentioning

confidence: 99%

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Zhang

2021

Wireless Communications and Mobile Computing

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“…An improved extrinsic monolingual plagiarism detection approach of the bengali text (Adil Ahnaf) 4257 based on the writing styles of the author, structural distributions, and vocabulary richness [1]. This study mainly focused on the extrinsic plagiarism detection approach.…”

Section: Introductionmentioning

confidence: 99%

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Ahnaf

Hasan

Sworna

et al. 2023

IJECE

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Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31%

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“…Although authorship attribution began with the stylistic analyses of humanities scholars (i.e., stylometry), with the advent of digital computers, the related techniques have been applied in music (e.g., musical style recognition and disputed musical authorship attribution; Brinkman et al, 2016; Tsai & Ji, 2020), art and painting (e.g., the identification of genuine paintings; Kokensparger, 2018; Yukimura et al, 2018), plagiarism detection (e.g., collaboration detection in documents; Gollub et al, 2013; AlSallal et al, 2019), spam detection (e.g., the detection of unsolicited and virus‐infested emails; Argamon et al, 2003; Rocha et al, 2017), and forensic investigation (e.g., author identification in anonymous or phishing emails; Gollub et al, 2013; Edwards, 2018). In the recent past, there has been increased research on code stylometry (Kokensparger, 2018; Kalgutkar et al, 2019; Quiring et al, 2019), which attempts to identify software authors from program source code using a feature analysis of programming styles.…”

Section: Introductionmentioning

confidence: 99%

A review on authorship attribution in text mining

Zheng

Jin

2022

WIREs Computational Stats

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The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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An integrated approach for intrinsic plagiarism detection

Cited by 31 publications

References 32 publications

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

A review on authorship attribution in text mining

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