POSNoise：一种有效抑制作者署名分析中主题偏向的方法
POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis
摘要：
作者身份验证是数字文本取证中的一项基础性研究任务，主要解决两篇文本是否出自同一人之手的问题。近年来，已经提出了各种各样的AV方法，专注于这个问题，可以分为两类：第一类是指基于显式定义的特征的方法，其中一个完全控制哪些特征被考虑以及它们实际代表什么。另一方面，第二类涉及基于隐式定义的特征的AV方法，其中不涉及控制机制，因此文本中的任何字符序列都可以用作潜在特征。然而，属于第二类的AV方法承担的风险，文本的主题可能会偏向他们的分类预测，这反过来又可能导致误导性的结论，他们的结果。为了解决这个问题，我们提出了一种名为POSNoise的预处理技术，它可以有效地屏蔽给定文本中与主题相关的内容。通过这种方式，AV方法被迫专注于与写作风格更相关的文本单元。我们基于六种AV方法（属于第二类）和七个语料库的经验评估表明，与42例中的34例中的知名主题掩蔽方法相比，POSNoise导致了更好的结果，准确性提高了10%。
英文摘要：
Authorship verification (AV) is a fundamental research task in digital text forensics, which addresses the problem of whether two texts were written by the same person. In recent years, a variety of AV methods have been proposed that focus on this problem and can be divided into two categories: The first category refers to such methods that are based on explicitly defined features, where one has full control over which features are considered and what they actually represent. The second category, on the other hand, relates to such AV methods that are based on implicitly defined features, where no control mechanism is involved, so that any character sequence in a text can serve as a potential feature. However, AV methods belonging to the second category bear the risk that the topic of the texts may bias their classification predictions, which in turn may lead to misleading conclusions regarding their results. To tackle this problem, we propose a preprocessing technique called POSNoise, which effectively masks topic-related content in a given text. In this way, AV methods are forced to focus on such text units that are more related to the writing style. Our empirical evaluation based on six AV methods (falling into the second category) and seven corpora shows that POSNoise leads to better results compared to a well-known topic masking approach in 34 out of 42 cases, with an increase in accuracy of up to 10%.
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