Reconstructing Arguments from Newsworthy Debates

社交媒体在当前的公共话语中越来越重要。在RANT中，我们的目标是提供从社交媒体讨论中发现的嘈杂文本中提取、表示和处理论点的方法和形式，使用大量公投前关于英国脱欧的Twitter消息作为运行案例研究。我们将进行语料库-语言学研究，以识别重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/低召回的方法从语料库中提取论点。事实上，我们期望能够将论证模式直接与专用形式体系中的逻辑模式关联起来，并相应地将单个参数直接解析为逻辑公式。论证表示的逻辑形式主义将以广泛的模式为特征，捕捉现实生活中的表达模式，如不确定性、代理、偏好、情感、模糊和默认。我们将把这种形式主义作为在共代数逻辑的一般逻辑框架中的实例逻辑家族，它为超出标准关系设置的模态提供统一的语义，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泛型共代数推理器的进一步发展。论证表示形式主义将由一个灵活的框架来补充，以表示论证之间的关系。这将包括标准关系，如Dung的攻击关系或支持关系，也包括从元数据中提取的关系，如引用，标签或直接地址（通过提到用户名），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，涉及模糊真值，偏好排序或概率，因此将有效地在协代数的统一框架中建模，这已经在上面作为协代数逻辑的语义基础出现。我们将为论证框架对Dung的扩展语义进行适当的概括，从而捕获诸如“连贯的观点”或“普遍的意见”等概念；结合相应的算法方法，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.