Authorship Attribution Using Small Sets of Frequent Part-of-Speech Skip-grams
Authorship Attribution Using Small Sets of Frequent Part-of-Speech Skip-grams
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发表时间：
2016
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通讯作者：
Yao Jean Marc Pokou;Philippe Fournier-Viger;C. Moghrabi
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作者：
Yao Jean Marc Pokou;Philippe Fournier-Viger;C. Moghrabi
计算机支持的作者归属提供了用于提取风格特征的工具，这些特征可以帮助验证或识别文本文档的作者。在许多情况下，找到文档的作者是非常重要的，例如在刑事调查期间检测剽窃以保护版权和法医支持。本文旨在探讨小说的文体特征，以准确地刻画作者的作品。特别是，使用部分的语音跳跃的克和内部的前k顺序模式挖掘算法被认为是作者归属的任务。一项研究使用了由10位作者撰写的30篇文本，包括2，615，856个单词和99，903个句子，证实了文本中的词性跳跃图的挖掘有助于作者身份的推断。
Computer-supported authorship attribution provides tools for extracting stylistic features that can help verify or identify the author of text documents. In many situations finding the author of a document is very important, such as the detection of plagiarism for protecting copy-rights and forensic support during criminal investigations. Thispaper, thus explores a novel stylistic feature with the aim of accurately characterizing an author’s work. In particular, the use of part-of-speech skip-grams and an in-house top-k sequential pattern mining algorithm are considered for the task of authorship attribution. A study using a collection of of 30 texts, written by 10 authors, consisting of 2 , 615 , 856 words and 99 , 903 sentences, confirms that mining part-of-speech skip-grams in texts facilitates authorship inference.