Quantitative Drama Analytics: Tracking Character Knowledge (Q:TRACK)

定量戏剧分析：跟踪角色知识（Q：TRACK）

• 批准号: 424244162
• 负责人: Professor Dr. Nils Reiter, since 3/2021
• 金额: --
• 依托单位: Germanistisches Seminar
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/424244162?language=en
• 关键词: Quantitative; Drama; Analytics; Tracking; Character

Ever since, dramatic plays were based on social relations, especially on family relations. Asymmetric distributions of knowledge about these relations are the main motivation for dramatic action (mythos) and dramatic effects. Research has repeatedly paid attention to this, but only in terms of small units as singular plays or oeuvres. Besides those close reading efforts there is still no attempt to investigate character knowledge about social relations in drama on a bigger scale. In Q:TRACK we aim at closing this gap by connecting techniques and methods from computational linguistics and digital humanities with the in-depth knowledge of literary studies about drama history and poetics.The first objective is situated in literary and drama history. By analysing a corpus of more than 600 plays we will quantify and specify the interconnections between social relations and dramatic conflicts throughout drama history. More precisely, we investigate when and how this social knowledge is transferred between characters. Real, merely assumed, or undisclosed social relations give reason to the dramatic interaction of the involved characters, which in turn also constitutes social relations.The second objective is to operationalize knowledge dissemination in the fictional worlds of drama. As it is the most important kind of knowledge in drama, we focus on social knowledge, i.e., knowledge about social relations between characters (“Orest is the brother of Elektra”). The technical and methodological aim is to develop annotation guidelines for instances of knowledge distribution and subsequently to detect such instances automatically within the dramatic text. With this, we will track the social knowledge for each character over the course of the play. In addition, we aim at extending the corpus base for quantitative drama research with methods for the semi-automatic detection of dramatic structure.The motivation for this undertaking is the relevance of character knowledge for their comprehension as literary beings. In particular, asymmetries and changes of knowledge that literary characters have (or don’t have) about the social relations in ‘their’ fictional worlds are constitutive for dramatic effect theories (catharsis) since antiquity. In comedy, confusing characters became genre-forming and led via Shakespeare to the "Comedy of errors" ("Verwechslungskomödie"). In tragedy, the oldest and most popular twists of mythos (peripeteia) are closely linked to the knowledge characters have about themselves or others (esp. anagnorisis).Therefore, the distribution and dissemination of social knowledge within the 'small world' of a play is central to our understanding as readers and researchers. Q:TRACK pilots the computational analysis of this knowledge by focusing on unambiguously defined and processable forms of knowledge.

从那时起，戏剧就以社会关系，尤其是家庭关系为基础。关于这些关系的知识的不对称分布是戏剧动作（神话）和戏剧效果的主要动机。研究已经多次注意到这一点，但只是在作为单一戏剧或作品的小单元方面。除了这些近距离的阅读努力之外，还没有尝试在更大的范围内调查戏剧中关于社会关系的人物知识。在Q：TRACK中，我们的目标是通过将计算语言学和数字人文学的技术和方法与文学研究中关于戏剧史和诗学的深入知识相结合来缩小这一差距。第一个目标位于文学和戏剧史。通过分析600多部戏剧的语料库，我们将量化和详细说明整个戏剧史上社会关系和戏剧冲突之间的相互联系。更确切地说，我们研究了这种社会知识何时以及如何在角色之间转移。 真实的、仅仅是假设的或未公开的社会关系使所涉及的人物之间的戏剧性互动成为理由，而这反过来也构成了社会关系。第二个目标是在戏剧的虚构世界中实现知识传播的可操作性。因为它是戏剧中最重要的知识，我们关注的是社会知识，即，关于人物之间社会关系的知识（“奥雷斯特是艾丽卡的兄弟”）。的技术和方法的目的是开发注释指南的知识分布的实例，并随后自动检测这种情况下的戏剧文本。有了这个，我们将在戏剧的过程中跟踪每个角色的社会知识。此外，我们的目标是扩大语料库的定量戏剧研究的方法，为戏剧结构的半自动检测。这项事业的动机是相关的字符知识，他们作为文学的理解。特别是，文学人物对“他们的”虚构世界中的社会关系的知识的不对称和变化是自古以来戏剧效果理论（宣泄）的组成部分。在喜剧中，令人困惑的角色成为体裁的形成，并通过莎士比亚导致了“错误的喜剧”（Verwechslungskomödie）。在悲剧中，最古老和最流行的神话转折（peripeteia）与人物对自己或他人的知识（特别是anagnorisis）密切相关。因此，社会知识在戏剧“小世界”中的分布和传播对于我们作为读者和研究者的理解至关重要。问：TRACK通过专注于明确定义和可处理的知识形式来引导对这些知识的计算分析。