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: 1813470
• 负责人: David Bamman
• 金额: $ 25万
• 依托单位: University of California-Berkeley
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813470&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.

该项目将开发并实证评估创建主观知识库的方法：即个人在书籍、网络论坛和社交媒体上断言的意见和观点的数据库。虽然大多数知识库研究试图从文本中提取现实世界的真理，但许多事实断言要么是关于固有的主观命题（如“苹果很好吃”），要么是碰巧与其他信念持有者甚至共识现实相矛盾的非主观断言（如“地球是平的”）。该项目开创了从文本语料库中自动提取意见和观点表达的新方法，并使用这些断言建立一个主观知识库，可以容纳来自不同作者的矛盾和冲突的陈述。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有哪些相互矛盾的说法？一个特定的意识形态团体持有什么样的主张，它们与其他团体持有的主张是相容的还是矛盾的？这个项目为理解广泛的现象奠定了基础，这些现象可以被看作是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，这些智能系统在现实世界中使用命题的方式方面是健壮的；随着人工智能的应用越来越多地应用于社会环境，这项研究将为这些方法提供更多关于人类观点多样性的细微信息。这项工作还将包括大量的教育内容，将人类背景纳入本科STEM教育的算法设计中，并在一系列学科中扩大自然语言处理和机器学习的使用。虽然以前的工作集中在识别文本中的确定性程度（信念，观点）的主要任务上，但这个项目的主要贡献将是通过观点持有者和他们所属的观点社区的变量来建模单个提取观点的结构。建立主观知识库的模型接受主观主张作为完全语义关系命题，如最近在开放信息提取方面的研究。然而，这些模型将不依赖于典型的跨文献共识假设，而是同时包含不同作者群体甚至同一个人作品中相互矛盾的主张。主要项目内容包括：开发和完善广泛领域的词性和句法分析，使其在社交媒体和历史书籍中都有效；使用这些工具来支持以作者为中心的结构化知识的潜在变量模型，该模型可以推断命题及其观点持有者的潜在立场；并通过对事实性和观点（信念）承诺的语言分析对模型进行改进。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Discovering Differences in the Representation of People using Contextualized Semantic Axes

使用情境化语义轴发现人们表征的差异

• 发表时间: 2022
• 期刊: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
• 作者: Lucy, Li;Tadimeti, Divya;Bamman, David
• 通讯作者: Bamman, David

Attending to Long-Distance Document Context for Sequence Labeling

• DOI: 10.18653/v1/2020.findings-emnlp.330
• 发表时间: 2020-11
• 作者: Matthew Jörke;Jon Gillick;Matthew Sims;David Bamman

Predicting Long-Term Citations from Short-Term Linguistic Influence

• DOI: 10.48550/arxiv.2210.13628
• 发表时间: 2022-10
• 作者: Sandeep Soni;David Bamman;Jacob Eisenstein

Characterizing English Variation across Social Media Communities with BERT

• DOI: 10.1162/tacl_a_00383
• 发表时间: 2021-02
• 期刊: Transactions of the Association for Computational Linguistics
• 影响因子: 10.9
• 作者: Li Lucy;David Bamman