III: Small: Collaborative Research: Building Subjective Knowledge Bases by Modeling Viewpoints

III：小：协作研究：通过建模观点构建主观知识库

• 批准号: 1814955
• 负责人: Brendan O'Connor
• 金额: $ 25万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814955&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Building

This project will develop and empirically evaluate methods for creating subjective knowledge bases: databases of opinions and viewpoints as they are asserted by individuals in books, web forums, and social media. While most knowledge base research seeks to extract real-world truth from text, many factual assertions are either about inherently subjective propositions (such as "apples are delicious") or are non-subjective assertions that happen to contradict other belief holders or even consensus reality (such as "the Earth is flat"). This project pioneers new methods to automatically extract expressions of opinions and viewpoints from a textual corpus and use those assertions to build a subjective knowledge base that can accommodate contradictory and conflicting statements from different authors. Such a subjective knowledge base will help researchers answer a range of questions: What contradictory claims are being made in historical books, or contemporary social media? What propositions does a particular ideological community hold, and are they compatible with, or contradictory to, those held by other communities? This project lays the foundation for understanding a broad range of phenomena that can be seen as conflicts between coherent viewpoints. The resulting computational models will lay the groundwork for intelligent systems that are robust with respect to the way in which propositions are used in the real world; as applications in artificial intelligence are being deployed more and more in social contexts, this research will inform these methods with more nuanced information about the diversity of human viewpoints. This work will also include a substantial educational component, incorporating human context into algorithm design in undergraduate STEM education and broadening the use of natural language processing and machine learning across a range of disciplines.While previous work has focused on the primary task of identifying degrees of certainty (belief, viewpoints) in text, the primary contribution of this project will be modeling the structure of individual extracted viewpoints through the variables of the viewpoint holders and the viewpoint communities to which they belong. Models for building subjective knowledge bases accept subjective claims as fully semantic relational propositions, like recent research in open information extraction. However, instead of relying on the typical assumption of cross-document consensus, these models will embrace the simultaneous presence of contradictory claims across different author groups or even within the writings of the same individual. Major project components include: developing and refining broad-domain part-of-speech and syntactic parsing to be effective across both social media and historical books; using these tools to support author-centric latent-variable models of structured knowledge, which infers latent positions for both propositions and their viewpoint-holders; and improving the model with linguistic analysis of factuality and viewpoint (belief) commitment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目将开发和经验性地评估创建主观知识库的方法：个人在书籍、网络论坛和社交媒体中断言的观点和观点数据库。虽然大多数知识库研究试图从文本中提取真实世界的真相，但许多事实断言要么是关于固有的主观命题(如苹果很美味)，要么是碰巧与其他信仰持有者甚至共识现实相矛盾的非主观性断言(如地球是平的)。这个项目开创了新的方法，可以自动从文本语料库中提取观点和观点的表达，并使用这些断言来构建一个主观知识库，该知识库可以容纳来自不同作者的相互矛盾和冲突的声明。这样一个主观的知识库将帮助研究人员回答一系列问题：历史书籍或当代社交媒体中提出了哪些相互矛盾的主张？一个特定的意识形态群体持有什么主张，它们与其他群体持有的主张是兼容的，还是矛盾的？该项目为理解可被视为连贯观点之间的冲突的广泛现象奠定了基础。由此产生的计算模型将为智能系统奠定基础，这些系统在现实世界中使用命题的方式是健壮的；随着人工智能中的应用越来越多地部署在社会背景中，这项研究将为这些方法提供关于人类观点多样性的更细微的信息。这项工作还将包括一个重要的教育组成部分，将人文语境纳入本科STEM教育的算法设计，并在一系列学科中广泛使用自然语言处理和机器学习。虽然以前的工作主要集中在识别文本中的确定性程度(信念、视点)，但该项目的主要贡献将是通过视点持有者和它们所属的视点社区的变量来建模个人提取的视点的结构。构建主观知识库的模型接受主观声明作为完全语义的关系命题，就像最近在开放信息提取方面的研究一样。然而，这些模式不依赖于跨文件共识的典型假设，而是包括不同作者群体甚至同一个人的作品中同时存在的相互矛盾的主张。主要项目组成部分包括：开发和改进广泛领域的词性和句法分析，以便在社交媒体和历史书籍中都有效；使用这些工具支持以作者为中心的结构化知识的潜在变量模型，该模型推断命题及其观点持有者的潜在立场；以及通过对事实和观点(信念)承诺的语言学分析来改进模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

How white is the global elite? An analysis of race, gender and network structure

全球精英有多白？

• DOI: 10.1111/glob.12309
• 发表时间: 2021
• 期刊: Global Networks
• 作者: YOUNG, KEVIN L.;GOLDMAN, SETH K.;O'CONNOR, BRENDAN;CHULUUN, TUUGI
• 通讯作者: CHULUUN, TUUGI

Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts

调查大量美式足球广播中体育评论员的偏见

• DOI: 10.18653/v1/d19-1666
• 发表时间: 2019
• 期刊: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP
• 作者: Merullo, Jack;Yeh, Luke;Handler, Abram;Grissom II, Alvin;O’Connor, Brendan;Iyyer, Mohit
• 通讯作者: Iyyer, Mohit

Modeling Financial Analysts’ Decision Making via the Pragmatics and Semantics of Earnings Calls

• DOI: 10.18653/v1/p19-1047
• 发表时间: 2019-06
• 作者: Katherine A. Keith;Amanda Stent

Paying Attention to the Algorithm Behind the Curtain: Bringing Transparency to YouTube's Demonetization Algorithms

• DOI: 10.1145/3555209
• 发表时间: 2022-11
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Arun Dunna;Katherine A. Keith;Narseo Vallina-Rodriguez;Rishab Nithyanand;Brendan T. O'Connor

ClioQuery: Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News Archives

• DOI: 10.1145/3524025
• 发表时间: 2022-04
• 期刊: ACM Transactions on Interactive Intelligent Systems (TiiS)
• 作者: Abram Handler;Narges Mahyar;Brendan T. O'Connor