EAGER: Investigating linguistic dimensions in cross-domain authorship analysis

分析文本并确定该文件作者的能力，一项被称为作者归属（AA）的任务，可以帮助建立一个针对网络滥用者的案件，确定文件的可信度，还可以通过分析在线社区的利益来支持这个国家打击恐怖主义。这项探索性研究的早期拨款在两个特定的跨领域设置中研究了AA的新方法：测试文档的主题和类型与训练数据的主题和类型不同。本研究脱离了文本分类设置中标准的单一特征向量表示，并遵循了一个框架，其中作者的书写印迹被表示为一组语言维度。目标是了解每个维度在新域中将如何变化。这项探索性研究的结果将表明，在训练数据和测试数据之间存在较大的域转移的情况下，构建新的文本表示和文本分类问题方法的可行性，并且将表示分解为语言维度是合适的。这项工作的结果和发现将有助于建立长期的研究项目，这将能够解决更具挑战性的跨领域设置。

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.