Relating the Unread. Network Models in Literary History

在文学史的混合方法中，我们打算使用文本关系的网络模型来研究分组是如何相对于文本语料库的编制方式而出现的。大数据的计算分析与数据本身一样重要。语料库几乎从来不是具有代表性的样本，如果不是故意收集的话，也是机会主义的。因此，它们的构成受到系统性偏见的影响，并影响文本之间关系的分析，文本相对于其他文本的位置和地位，以及文本的分类。我们将开发网络模型，专门为18世纪和19世纪的德语和欧洲英语文学集成文体，语义和注释数据，专注于小说和叙事。我们的目标是在这个决定性的时期，对类型惯例、子类型和时代风格的建立提出不同的看法。我们从社会历史的角度关注文学史进程中被边缘化的文本和作者，特别关注女性作家及其与经典文本的地位。除了开发模型和算法外，我们还将使用语料库操作作为启发式方法来研究分组过程以及共时和历时语料库比较。通过检测某个数据层上的组，并观察它们在另一个数据层上消失，我们将能够培养我们对历史和系统文本关系的理解，以及对不同时期文本组的理解。

In a mixed methods approach to literary history, we intend to use network models of text relations to study how groupings emerge relative to the way text corpora have been compiled.Computational analysis of big data is as much about analysis as it is about the data itself. Corpora are almost never representative samples but opportunistic if not deliberate collections. Their composition is thus subject to systematic biases and affects the analysis of relations among texts, positions and status of texts relative to others, and the categorization of texts into groups.We will develop network models that integrate stylometric, semantic, and annotation data specifically for German-speaking and European Anglophone literatures of the 18th and 19th century, concentrating on novels and narratives. Our goal is to shine a different light on the establishment of genre conventions, subgenres and epoch style within this decisive period. From a socio-historical view point we focus on those texts and authors who were marginalized in the course of literary history, paying special attention to female writers and their positions alongside canonized texts.In addition to developing models and algorithms, we will use corpus manipulation as a heuristic method to study grouping processes and combined synchronic and diachronic corpus comparison. By detecting groups on a certain data level and watch them disappear on another, we will be able to foster our understanding of both historical and systematic text relations, and also of text groups within different periods of time.