Anomaly-based large-scale analysis of style and genre reflected in the use of stylistic devices in medieval literature

该项目的目标是开发一种新的远程阅读工具，用于定量分析修辞手段的使用。开发的方法将应用于三个中世纪体裁的比较文体分析：中古高地德语改编的三部作品。从中世纪研究的角度来看，我们的目标是确定古代浪漫小说，亚瑟王浪漫小说和12世纪和13世纪的香颂de geste改编之间的体裁特定风格差异。到目前为止，这些文体在修辞手法上的异同还没有得到量化的分析。这样的分析对于中世纪“体裁”概念和中世纪作家体裁意识的争议性讨论以及评估中世纪学校教授的修辞手段与白话作家实践的相关性具有价值。为此，我们打算分析修辞手段的选择、使用和频率是否取决于场景类型，以及特征差异是否可以归因于类型或不同的作者或起源日期。如此广泛的分析需要远距离阅读的计算方法，而这种方法尚不存在，无法在令人满意的程度上检测文体装置，更不用说中古高地德语文本了。为此，我们寻求基于异常检测以及主动和终身机器学习领域的方法来开发检测修辞装置的新方法。在这种情况下，我们认为修辞手段是“异常”，是对大部分语料库所建立的数量规范的偏离，可以用统计方法检测到（例如，交错是非交错结构中的异常）。为了将耗时的手动注释任务减少到最低限度，我们建议在人类参与的半监督场景中采用主动学习技术：基于检测文体异常的初始无监督过程，系统通过主动要求用户对一些信息丰富的示例进行注释，在交互式过程中改进自身。此外，我们打算将专家提供的先前理论知识整合到异常检测程序中，以过滤掉明显的误报并将其转向某些修辞手段。在这个项目中，我们将重点关注两种典型的文体手段：作为重复形象的交错和作为修辞的隐喻。后者也可以被视为一种异常现象，因为来自不同领域的词之间存在不寻常的相互作用，这反映在它们的词嵌入中。由于这样的嵌入需要从庞大的语料库中学习，这对于像中古高地德语这样的低资源语言来说是不可用的，我们也努力开发一种技术来适应在新高地德语语料库上学习到的这种嵌入到中古高地德语。

The goal of this project is the development of a novel distant-reading tool for the quantitative analysis of the use of rhetorical devices. The developed methods will be applied for a comparative stylometric analysis of three medieval genres: the Middle High German adaptations of the trois matières. From the viewpoint of medieval studies, we aim for identifying genre-specific stylistic differences between romances of antiquity, Arthurian romances, and chanson de geste adaptations of the 12th and 13th century. Up to now, quantifiable similarities and differences of these genres regarding their use of rhetorical devices have not been analysed. Such an analysis would be valuable for the controversial discussion about the medieval concepts of ‘genre’ and the genre-awareness of medieval authors as well as for assessing the relevance of rhetorical devices taught in medieval schools for the practice of vernacular authors. To this end, we intend to analyse whether selection, use, and frequency of rhetorical devices depend on the scene type and whether characteristic differences can be attributed to genres or just different authors or dates of origin. Such a broad analysis requires computational methods for distant reading, which do not yet exist for the detection of stylistic devices to a satisfactory degree, not to mention for Middle High German texts. For this purpose, we seek to develop novel methods for detecting rhetorical devices based on methods from the fields of anomaly detection as well as active and life-long machine learning. In this context, we consider rhetorical devices as ‘anomalies’, as deviations from the quantitative norm established by the greater part of the corpus, which can be detected using statistical methods (e.g., a chiasmus is an anomaly amidst non-chiastic constructions). To reduce time-consuming manual annotation tasks to a minimum, we propose to employ active learning techniques in a semi-supervised scenario with the human in the loop: Based on an initially unsupervised process of detecting stylistic anomalies, the system refines itself in an interactive process by actively asking the user for annotations for a few informative examples. Furthermore, we intend to integrate prior theoretical knowledge provided by experts into the anomaly detection procedure for filtering out obvious false positives and steering it towards certain rhetorical devices. In this project, we will focus on two exemplary stylistic devices: the chiasmus as a figure of repetition and the metaphor as a trope. The latter can be seen as an anomaly as well due to the unusual interactions between words from different domains, reflected in their word embeddings. Since such embeddings need to be learned from huge corpora, which are not available for low-resource languages such as Middle High German, we also strive for developing a technique to adapt such embeddings learned on New High German corpora to Middle High German.