EAGER: Investigating linguistic dimensions in cross-domain authorship analysis

EAGER：研究跨领域作者分析中的语言维度

• 批准号: 1254108
• 负责人: Steven Bethard
• 金额: $ 4.51万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254108&HistoricalAwards=false
• 关键词: EAGER; Investigating; linguistic; dimensions; cross

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.

分析文本并确定该文档作者的能力，即作者归属（AA），可以帮助建立针对在线滥用者的案例，确定文档的可信度，还可以通过分析在线社区来支持这个国家的反恐斗争。 EArly的探索性研究基金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。该研究从文本分类设置中的标准单一特征向量表示出发，并遵循一个框架，其中作者的writeprint表示为一组语言维度。 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显示建立新的文本表示和文本分类问题方法的可行性，其中训练和测试数据之间存在较大的域转移，并且语言维度的突破表示是合适的。这项工作的结果和发现将有助于建立长期的研究项目，这些项目将能够解决更具挑战性的跨领域环境。