Reconstructing Arguments from Newsworthy Debates

从有新闻价值的辩论中重构论点

• 批准号: 377333057
• 负责人: Professorin Dr. Stephanie Evert
• 金额: --
• 依托单位: Informatik 8 - Theoretische Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/377333057?language=en
• 关键词: Reconstructing; Arguments; Newsworthy; Debates

Social media are of increasing importance in current public discourse. In RANT, we aim to contribute methods and formalisms for the extraction, representation, and processing of arguments from noisy text found in discussions on social media, using a large corpus of pre-referendum Twitter messages on Brexit as a running case study. We will conduct a corpus-linguistic study to identify recurring linguistic argumentation patterns and design corresponding corpus queries to extract arguments from the corpus, following a high-precision/low-recall approach. In fact, we expect to be able to associate argumentation patterns directly with logical patterns in a dedicated formalism and accordingly parse individual arguments directly as logical formulas. The logical formalism for argument representation will feature a broad range of modalities capturing real-life modes of expression such as uncertainty, agency, preference, sentiment, vagueness, and defaults. We will cast this formalism as a family of instance logics in the generic logical framework of coalgebraic logic, which provides uniform semantic, deductive and algorithmic methods for modalities beyond the standard relational setup; in particular, reasoning support for the logics in question will be based on further development of an existing generic coalgebraic reasoner. The argument representation formalism will be complemented by a flexible framework for the representation of relationships between arguments. These will include standard relations such as Dung's attack relation or a support relation but also relations extracted from metadata such as citation, hashtags, or direct address (via mention of user names), as well as relationships that are inferred from the logical content of individual arguments. The latter may take on a non-relational nature, involving, e.g., fuzzy truth values, preference orderings, or probabilities, and will thus fruitfully be modelled in the uniform framework of coalgebra that has already appeared above as the semantic foundation of coalgebraic logic. We will develop suitable generalizations of Dung's extension semantics for argumentation frameworks, thus capturing notions such as `coherent point of view' or `pervasive opinion'; in combination with corresponding algorithmic methods, these will allow for the automated extraction of large-scale argumentative positions from the corpus.

社交媒体在当前的公共话语中越来越重要。在RANT中，我们的目标是提供方法和形式主义，用于从社交媒体讨论中发现的嘈杂文本中提取，表示和处理参数，使用大量关于英国脱欧的公投前Twitter消息作为运行案例研究。我们将进行语料库语言学研究，以确定反复出现的语言论证模式，并设计相应的语料库查询，从语料库中提取参数，遵循高精度/低召回的方法。事实上，我们希望能够在一个专门的形式主义中直接将论证模式与逻辑模式相关联，并相应地将单个论证直接解析为逻辑公式。论证表征的逻辑形式主义将以广泛的模态为特征，这些模态捕捉了现实生活中的表达模式，如不确定性、代理、偏好、情感、模糊性和默认。我们将铸造这种形式主义作为一个家庭的实例逻辑的通用逻辑框架的coalgebraic逻辑，它提供了统一的语义，演绎和算法方法的模态超出了标准的关系设置，特别是，推理支持的逻辑问题将基于进一步发展现有的通用coalgebraic推理。论证表示形式主义将由一个灵活的框架来补充，用于表示论证之间的关系。这些将包括标准关系，例如Dung的攻击关系或支持关系，但也包括从元数据中提取的关系，例如引用，主题标签或直接地址（通过提及用户名），以及从单个参数的逻辑内容推断的关系。后者可能具有非关系性质，例如，模糊真值、偏好排序或概率，因此将在余代数的统一框架中有效地建模，该框架已经在上面作为余代数逻辑的语义基础出现。我们将开发合适的概括的Dung的扩展语义的论证框架，从而捕捉的概念，如“连贯的观点”或“普遍的意见”，结合相应的算法方法，这些将允许自动提取大规模的论证立场从语料库。