Figure plagiarism detection based on textual features representation

T. Eisa, N. Salim, Salha M. Alzahrani
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引用次数: 3

Abstract

In an academic environment, plagiarism is the process of copying someone else's text, idea or data verbatim or without due recognition of the source, which is a serious academic offence. Many techniques have been proposed in the literature for detecting plagiarism in texts, but only a few techniques exist for detecting figure plagiarism. The main problem associated with existing techniques of plagiarism detection is that they are not applicable to non-textual elements of figures in research publications. This paper focuses on detecting plagiarism in scientific figures. Textual-reference representation based figure plagiarism detection techniques are proposed and evaluated, based on existing limitations. The proposed techniques use enhanced feature extraction such as textual features and similarity computation methods such as similarity based on textual-reference of figures. The enhanced feature extraction method was found to be capable of extracting textual references such as captions and description texts. The similarity detection method was capable of categorising a given figure as either plagiarised or non-plagiarised from a source collection of scientific publications, depending on a certain threshold value. Results showed that the proposed technique achieved precision=0.78 and recall=0.67 result in terms of the evaluation measure.
基于文本特征表示的图形抄袭检测
在学术环境中,抄袭是指逐字复制他人的文本、想法或数据的过程,或者没有对来源进行适当的承认,这是一种严重的学术犯罪。文献中已经提出了许多检测文本剽窃的技术,但检测图形剽窃的技术却很少。与现有的剽窃检测技术相关的主要问题是,它们不适用于研究出版物中数字的非文本元素。本文主要研究科学人物的抄袭检测问题。基于文本参考表示的图形剽窃检测技术在现有局限性的基础上被提出并评估。所提出的技术采用增强的特征提取方法,如文本特征和相似度计算方法,如基于文本参考的图形相似度。改进后的特征提取方法能够提取文本引用,如标题和描述文本。相似性检测方法能够根据一定的阈值将科学出版物来源集合中的给定图形分类为抄袭或非抄袭。结果表明,该方法在评价指标上的准确率为0.78,召回率为0.67。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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