SDCI Data New: A Modular Software Framework for Evaluation, Testing, and Cross-Fertilization of Authorship Attribution Techniques

SDCI 数据新功能：用于作者归属技术评估、测试和交叉应用的模块化软件框架

• 批准号: 0721667
• 负责人: Patrick Juola
• 金额: $ 21.2万
• 依托单位: Duquesne University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721667&HistoricalAwards=false
• 关键词: SDCI; Data; New; Modular; Software

A Modular Software Framework for Evaluation, Testing, and Cross-Fertilization of Authorship Attribution TechniquesPI: Patrick M. Juola, PhD, Duquesne UniversityDocuments do not speak only of their contents; to a trained eye, they can also say much about their author. The field of ``authorship attribution'' in humanities scholarship has been attending to this for centuries, trying to determine how and to what accuracy the author of a document can be determined. Recent developments in corpus linguistics have shown it to be possible to make these determinations automatically by ``non-traditional'' methods, essentially statistical investigations of the words, phrases, layout, and other features of the document. Unfortunately, the current state-of-the-art is a confused collection of proposed methods, with little guidance about which methods work, why they work, and under what conditions they work best. We are addressing this by developing a modular software framework (using a theoretical model proposed by Juola[23]) to perform this task in a modular design that permits easy swapping of functional components in cross-combination. By applying a rigorous testing method to the resulting set of (novel) combinations, the project is establishing accuracy benchmarks for various techniques (under the various testing conditions), finding new combinations resulting in improved techniques, and creating "best practices."

一个模块化软件框架，用于评估，测试和交叉归因技术技术：Patrick M. Juola博士，Duquesne UniversityDocuments博士不仅仅在于其内容；在训练有素的眼中，他们也可以对作者说很多话。 数百年来，人文学术奖学金中``作者归因''的领域已经参与其中，试图确定可以确定文件的作者的准确性以及如何确定。 语料库语言学的最新发展表明，可以通过``非传统''方法自动做出这些确定，这基本上是对文档的单词，短语，布局和其他特征的统计研究。不幸的是，当前的最新方法是拟议方法的混乱集合，几乎没有指导哪种方法起作用，它们的工作方式以及在哪种条件下效果最佳。 我们正在通过开发模块化软件框架（使用Juola [23]提出的理论模型）来解决此问题，以在模块化设计中执行此任务，该模块设计允许在交叉组合中轻松地将功能组件交换。通过将严格的测试方法应用于所得的（新颖）组合集，该项目正在为各种技术（在各种测试条件下）建立准确的基准测试，从而找到新的组合，从而改善了技术，并创建了“最佳实践”。