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

基本信息

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

项目摘要

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.

该项目将开发并实证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主张的观点和观点的数据库。虽然大多数知识库研究试图从文本中提取现实世界的真相，但许多事实断言要么是关于固有的主观命题（例如“苹果很好吃”），要么是恰好与其他信念持有者甚至共识现实相矛盾的非主观断言（例如“地球是平的”）。该项目开创了新方法，从文本语料库中自动提取意见和观点的表达，并使用这些断言构建主观知识库，以适应不同作者的矛盾和冲突的陈述。这样的主观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，它们与其他群体所持的主张是一致还是矛盾？该项目为理解广泛的现象奠定了基础，这些现象可以被视为连贯观点之间的冲突。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方面具有鲁棒性；随着人工智能的应用越来越多地部署在社会环境中，这项研究将为这些方法提供有关人类观点多样性的更细致的信息。这项工作还将包括实质性的教育部分，将人类背景融入本科 STEM 教育的算法设计中，并扩大自然语言处理和机器学习在多个学科中的使用。由于文本中的确定性（信念、观点），该项目的主要贡献将是通过观点持有者及其所属观点社区的变量对各个提取的观点的结构进行建模。构建主观知识库的模型接受主观主张作为完全语义关系命题，就像最近在开放信息提取方面的研究一样。然而，这些模型并不依赖于跨文档共识的典型假设，而是包含不同作者群体甚至同一个人的著作中同时存在的矛盾主张。主要项目组成部分包括：开发和完善广泛领域的词性和句法解析，使其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位置；通过对事实和观点（信念）承诺的语言分析来改进模型。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。