Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is: is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.
翻译：学术上的破坏是当今一个严重的问题。由于数字信息源无穷无尽的存在,今天比以往任何时候更容易造成破坏。好的是,破坏性检测技术已经得到改善,而且足以探测教育中的破坏性尝试。我们现在看到的是有效的破坏性检测软件,如Turnitin、当机或SafeAsign。在导言中,我们探讨了克罗地亚学术界用来在大学和/或科学期刊中进行破坏性检测的软件。问题是:这已经足够吗?目前的软件已证明是成功的,然而,识别抛光镜或蒙糊糊糊的损害性识别问题仍然没有解决。在本文中,我们提交了一份报告,说明如何在植被探测任务中使用语言相似的测量措施。
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