RI: CAREER : Understanding Opinions by Reasoning over Socially Grounded Language

RI：职业：通过对社会基础语言进行推理来理解观点

• 批准号: 2048001
• 负责人: Dan Goldwasser
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048001&HistoricalAwards=false
• 关键词: RI; CAREER; Understanding; Opinions; Reasoning

Social media platforms have recently emerged as the primary space for public conversations, providing a venue for people to share perspectives and for policymakers to promote their decisions and inform the public about them. The massive amount of available opinion data presents tantalizing opportunities to study the perspectives expressed on these platforms. Insights derived from this analysis can help gauge public opinion, inform public policy, and help support human decision making. Realizing these opportunities requires models adapted to the new social media settings, in which linguistic content and its social context cannot be separated. This CAREER project develops novel modeling techniques and learning algorithms for combining these two aspects under a common innovative principle -- creating a socially grounded language representation that views opinion understanding as part of a larger framework of understanding real-world scenarios (such as the implementation of specific policies or the response to an emergency situation) and their participants. This research helps provide the relevant context needed for better understanding social media content and result in highly nuanced analysis, capturing the stances, attitudes and relationships between the different stakeholders of a given real-world scenario.This project suggests a new way to conceptualize opinionated text analysis, as part of a real-world scenario, reflecting the attitudes-of and relationships-between stakeholders in the scenario from which the text emerges. A major design goal is to avoid the supervision bottleneck, and allow the system to easily adapt to new events and policy issues by using the social information associated with users as a form of indirect supervision over documents they author. This is done by representing documents, authors, referenced entities, their connections and behaviors in a shared neuro-symbolic framework enabling symbolic inference over latent entity representations learned from data. The project addresses three main challenges: (1) constructing a representation language for characterizing opinions, their targets and motivation, and the stances they express, (2) grounding opinion text in real world scenarios by infusing relevant real-world information into a neural language model, and (3) exploiting social information by formulating a unified view of social, behavioral, and textual information. These research efforts help provide nuanced insights from social media content that lacks specificity on its own, while building the computational foundations for jointly processing textual and social information.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.

社交媒体平台最近已成为公众对话的主要空间，为人们分享观点和决策者宣传其决定并向公众通报这些决定提供了一个场所。大量可用的意见数据为研究这些平台上表达的观点提供了诱人的机会。从这种分析中获得的见解可以帮助衡量民意，为公共政策提供信息，并帮助支持人类决策。实现这些机会需要适应新的社会媒体环境的模式，在这种环境中，语言内容及其社会背景是不可分割的。这个CAREER项目开发了新颖的建模技术和学习算法，用于在一个共同的创新原则下将这两个方面结合起来-创建一个基于社会的语言表示，将意见理解视为理解现实世界场景（例如实施特定政策或应对紧急情况）及其参与者的更大框架的一部分。 这项研究有助于提供更好地理解社交媒体内容所需的相关背景，并导致高度细致的分析，捕捉给定现实世界场景中不同利益相关者之间的立场，态度和关系。该项目提出了一种新的方式来概念化固执己见的文本分析，作为现实世界场景的一部分，反映了在产生文本的情景中利益相关者之间的态度和关系。一个主要的设计目标是避免监督瓶颈，并允许系统轻松地适应新的事件和政策问题，通过使用与用户相关联的社会信息作为一种形式的间接监督他们的文件作者。这是通过在一个共享的神经符号框架中表示文档、作者、引用实体、它们的连接和行为来实现的，该框架能够对从数据中学习到的潜在实体表示进行符号推理。该项目解决了三个主要挑战：（1）构建表征意见的表示语言，他们的目标和动机，以及他们表达的立场，（2）通过将相关的真实世界信息注入神经语言模型，将意见文本建立在真实的世界场景中，以及（3）通过制定社会，行为和文本信息的统一视图来利用社会信息。这些研究工作有助于从社交媒体内容中提供细致入微的见解，同时为联合处理文本和社交信息建立计算基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。