SDCI Data: Improvement: Java Graphical Authorship Attribution Program (JGAAP)

机器学习和语料库语言学的最新发展表明，使用统计数据自动确定作者身份是可能的；NSF资助的JGAAP （Java图形作者归属程序）系统是这些发展的一部分。JGAAP帮助支持了新兴的作者归属社区，并为各种学术专业创建了一个有用的工具。虽然JGAAP包含了数千种可能的方法，但在文献中有更多的方法已经被提出，但没有经过严格的测试。大规模的比较性测试需要开发新的方法和测试语料库。此外，还有许多关键问题需要解决，以满足社区的需求，如公开课问题，对抗性问题，以及合作问题。最后，我们将研究JGAAP和类似系统在语言分析的关键领域的应用，例如确定性别、教育、母语、心理特征、医疗状况、年龄（文件或作者），甚至企图欺骗。同样，通过对这些新问题和语料库应用严格的测试方法，项目可以为各种技术（在各种测试条件下）建立准确性基准，找到导致技术改进的新组合，并建立“最佳实践”建议。改进的作者归属将立即对学者和更广泛的社会背景有用，例如对这种安全技术有直接需求的执法和法医。历史/社会分析还将为数字人文、社会学、历史学和计算机科学等相关学科之间提供更好的连接，为更好地理解传统人文问题提供基础。侧写工作可以通过提供一种非侵入性的方法来检测一个人思想的某些方面，从而帮助医学和心理学从业者。开发的软件（以及计划的开发/分发过程）将有助于提高数字人文学术和计算机科学的有效性，特别是通过建立软件审查标准和过程。特别是，通过提供各种技术所涉及的条件和预期错误率的直接证据，所获得的信息将有助于作者归属符合专家证据的道伯特标准，从而允许作者归属在正式的法律环境中使用。最后，这项研究的资金将有助于支持杜肯大学独特的跨学科计算数学项目，为技术教育提供更广泛的途径。

Recent developments in machine learning and corpus linguistics have shown it to be possible to make automatic determinations about authorship using statistics; the NSF- funded JGAAP (Java Graphical Authorship Attribution Program) system has been part of these developments. JGAAP has helped support the emerging authorship attribution community and create a useful tool for a wide variety of scholastic specialties. Although JGAAP incorporates thousands of possible methods, there are many more in the literature that have been proposed but not rigorously tested. Comparative testing on a large scale will require the development of new methods and test corpora. In addition, there are many key problems to address to meet the needs of the community, such as the open class problem, the adversarial problem, and the coauthorship problem. Finally, we will examine applications of JGAAP and similar systems to key areas in linguistic profiling, such as determining gender, education, native language, psychological profile, medical condition, age (of document or writer), or even attempted deceptiveness. Again, by applying a rigorous testing method to these new problems and corpora, the project can establish accuracy benchmarks for various techniques (under the various testing conditions), find new combinations resulting in improved techniques, and establish a recommendation for 'best practices.' Improved authorship attribution will be immediately useful both to scholars and in broader social contexts, such as law enforcement and forensics where there are direct demands for this kind of security technology. The historical/social analysis will also provide better access between the related disciplines of digital humanities, sociology, history, and computer science, providing the basis for a better understanding of traditional humanities issues. Profiling work can help medical and psychological practitioners by providing a non-invasive method to detect certain aspects of a person's mind. The software developed (and the planned development/distribution process) will help improve the effectiveness of both digital humanities scholarship and computer science, especially through the establishment of software review standards and processes. In particular, by providing direct evidence of the conditions and expected error rates involved in various techniques, the information gained will help authorship attribution meet the Daubert criteria for expert evidence, allowing authorship attribution to be used in a formal legal setting. Finally, the funding of this research will help support the unique interdisciplinary Duquesne University Computational Mathematics program, providing a broader access to an unusual and atypical audience for technological education.