EAGER: Investigating linguistic dimensions in cross-domain authorship analysis

分析文本并确定文档作者的能力，即所谓的作者归属(AA)任务，可以帮助建立针对在线施虐者的案件，确定文档的可信度，还可以通过分析在线感兴趣的社区来支持该国的反恐斗争。这一早期的探索性研究拨款在两个特定的跨域环境中调查了AA的新方法：测试文档的主题和体裁都与训练数据的主题和体裁不同。这项研究偏离了文本分类环境中的标准单一特征向量表征，遵循了作者的手写文字被表示为一组语言维度的框架。我们的目标是了解每个维度在新领域中将如何变化。这项探索性研究的结果将表明，对于训练数据和测试数据之间存在较大域漂移的文本分类问题，建立新的文本表示和方法是可行的，并且适合将突破性表示扩展到语言维度。这项工作的结果和发现将有助于建立更长期的研究项目，这些项目将能够应对更具挑战性的跨领域环境。

The ability to analyze a text and determine with certainty the author of that document, a task known as Authorship Attribution (AA), can help build a case against an online abuser, determine the trustworthiness of a document, and can also support this country's fight against terrorism by analyzing online communities of interest. This EArly Grant for Exploratory Research investigates new approaches for AA in two specific cross-domain settings: where both the topic and genre of the test documents differ from those of the training data. The research study departs from the standard single feature vector representation in text classification settings and follows a framework where the writeprint of authors is represented as a set of linguistic dimensions. The goal is to understand how each dimension will change in the new domain. The findings from this exploratory research will show the feasibility of building new text representations and approaches for text classification problems where there are larger domain shifts between the training and testing data and the breakout representation into linguistic dimensions is suitable. The results and findings from this work will contribute to building longer-term research projects which will be able to tackle more challenging cross-domain settings.