Reconstructing Arguments from Newsworthy Debates

社交媒体在当前的公共话语中变得越来越重要。在RANT中，我们的目标是使用公投前关于英国退欧的大量推文语料库作为一个正在进行的案例研究，为从社交媒体上的讨论中发现的嘈杂文本中提取、表示和处理论点提供方法和形式化。我们将进行语料库-语言学研究，以识别反复出现的语言论证模式，并设计相应的语料库查询，以遵循高精度/低召回率的方法从语料库中提取论点。事实上，我们希望能够在专门的形式主义中将论证模式与逻辑模式直接关联，并相应地将单个论点直接解析为逻辑公式。论元表达的逻辑形式主义将以广泛的形式为特色，这些形式捕捉现实生活中的表达方式，如不确定性、代理、偏好、情感、含糊和缺省。我们将把这种形式主义描述为协代数逻辑的通用逻辑框架中的一族实例逻辑，它为标准关系设置之外的模态提供了统一的语义、演绎和算法方法；特别是，对所讨论的逻辑的推理支持将基于现有的通用余代数推理器的进一步发展。参数表示形式将由用于表示参数之间关系的灵活框架来补充。这些将包括标准关系，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，如引用、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。后者可呈现非关系性质，涉及例如模糊真值、偏好排序或概率，并且因此将在作为余代数逻辑的语义基础出现在上面的统一的余代数框架中被有效地建模。我们将为论证框架开发Dung的扩展语义的适当概括，从而捕获“连贯的观点”或“普遍的观点”等概念；与相应的算法方法相结合，这些将允许从语料库中自动提取大规模的论证立场。

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.