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首页> 外文会议>2014 IEEE/ACM Joint Conference on Digital Libraries >Reducing computational effort for plagiarism detection by using citation characteristics to limit retrieval space
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Reducing computational effort for plagiarism detection by using citation characteristics to limit retrieval space

机译：通过使用引文特征限制检索空间，减少抄袭检测的​​计算量

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摘要

This paper proposes a hybrid approach to plagiarism detection in academic documents that integrates detection methods using citations, semantic argument structure, and semantic word similarity with character-based methods to achieve a higher detection performance for disguised plagiarism forms. Currently available software for plagiarism detection exclusively performs text string comparisons. These systems find copies, but fail to identify disguised plagiarism, such as paraphrases, translations, or idea plagiarism. Detection approaches that consider semantic similarity on word and sentence level exist and have consistently achieved higher detection accuracy for disguised plagiarism forms compared to character-based approaches. However, the high computational effort of these semantic approaches makes them infeasible for use in real-world plagiarism detection scenarios. The proposed hybrid approach uses citation-based methods as a preliminary heuristic to reduce the retrieval space with a relatively low loss in detection accuracy. This preliminary step can then be followed by a computationally more expensive semantic and character-based analysis. We show that such a hybrid approach allows semantic plagiarism detection to become feasible even on large collections for the first time.
机译：本文提出了一种学术文献中窃检测的混合方法，该方法将引用，语义参数结构和语义词相似度的检测方法与基于字符的方法相结合，以实现对变相窃形式的更高检测性能。当前可用的for窃检测软件专门执行文本字符串比较。这些系统可以找到副本，但无法识别出隐喻的窃，例如释义，翻译或思想窃。与基于字符的方法相比，存在在单词和句子级别上考虑语义相似性的检测方法，并且这些方法始终能够实现对伪装抄袭形式的更高检测精度。但是，这些语义方法的大量计算工作使它们在现实抄袭检测场景中无法使用。提出的混合方法使用基于引用的方法作为一种初步的启发式方法，以减少检索空间，同时降低了检测精度。然后，可以在此初步步骤之后进行计算上更昂贵的基于语义和字符的分析。我们表明，这种混合方法使语义窃检测即使在第一次大型馆藏中也变得可行。

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