CAREER: Authorship Analysis in Cross-Domain Settings

职业：跨域设置中的作者分析

• 批准号: 1462141
• 负责人: Thamar Solorio
• 金额: $ 46.96万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462141&HistoricalAwards=false
• 关键词: CAREER; Authorship; Analysis; Cross; Domain

Authorship Analysis (AA) is the task of extracting characteristics from written documents that can help to determine authorship of a document, generate a profile of the author, or identify cases of plagiarism. AA can be used for historical purposes, to settle disputes over the original creators of a given document, and to build a prosecution case against an online abuser. Most previous work in AA assumes the availability of samples with known authorship that closely match the domain of the documents of interest. A strong assumption like this one limits the applications of AA approaches. This program addresses this key outstanding challenge by designing robust frameworks for scenarios with different cross-domain degrees: cross-topic, cross-genre and cross-modality (text vs. transcribed speech). The project leverages the large amounts of free text available representing each cross-domain setting to learn general lexical and syntactic distributional correspondences. These correspondences are used to map the out-of-domain texts to a representation that is closer to the target domain. Direct contributions of this research include new approaches to extract and embed cross-domain prior knowledge into AA models in the form of distributional trajectories; and a solid understanding of the influence of topic, genre, and modality in the feature engineering process for AA that will also be helpful in other text processing tasks. This research will make direct contributions to the field of forensic linguistics, which is of major relevance for national security.The PI will design an advanced seminar in computational approaches for forensic linguistics and will expand her ongoing educational and outreach activities for underrepresented groups in the STEM disciplines. The PI will integrate opportunities for international visits to key research labs for the graduate students involved in the program that will enrich their training and provide great networking opportunities.

作者身份分析（AA）是从书面文档中提取特征的任务，这些特征可以帮助确定文档的作者身份，生成作者简介或识别剽窃案例。AA可以用于历史目的，解决给定文件的原始创建者的争议，并对在线滥用者提起诉讼。在AA的大多数以前的工作假设与已知的作者密切匹配的领域感兴趣的文档的样本的可用性。这样一个强有力的假设限制了AA方法的应用。该计划通过为具有不同跨领域程度的场景设计强大的框架来解决这一关键的突出挑战：跨主题，跨体裁和跨模态（文本与转录语音）。 该项目利用大量的自由文本，代表每个跨域设置，学习一般的词汇和句法分布对应。这些对应关系用于将域外文本映射到更接近目标域的表示。这项研究的直接贡献包括新的方法来提取和嵌入跨域先验知识到AA模型的形式分布轨迹;和一个坚实的理解主题，体裁和模态的影响，在特征工程过程中的AA，也将有助于其他文本处理任务。这项研究将直接贡献于法律语言学领域，这是与国家安全的重大关系。PI将设计一个高级研讨会在计算方法的法律语言学，并将扩大她正在进行的教育和推广活动，为代表性不足的群体在STEM学科。PI将为参与该计划的研究生提供国际访问主要研究实验室的机会，这将丰富他们的培训并提供很好的网络机会。