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辑方式出现的。大数据的计算分析既是关于数据本身的分析，也是关于分析的。语料库几乎从来都不是有代表性的样本，而是机会主义的，如果不是故意收集的话。因此，它们的构成受到系统性偏见的影响，并影响对文本之间的关系、文本相对于他人的位置和地位的分析，以及对文本的分类。我们将开发专门针对18世纪和19世纪的德语和欧洲英语文学的网络模型，将重点放在小说和叙事上，整合文体、语义和注释数据。我们的目标是在这个决定性的时期，以不同的方式来建立体裁惯例、亚体裁和时代风格。从社会历史的角度，我们关注在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在经典化文本中的地位；除了开发模型和算法外，我们还将使用语料库操纵作为启发式方法来研究分组过程，并结合共时和历时语料库进行比较。通过检测特定数据级别上的组并观察它们在另一个数据级别上的消失，我们将能够促进我们对历史和系统的文本关系以及不同时间段内的文本组的理解。

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.