Is Expert Knowledge Key? Scholarly Interpretations as Resource for the Analysis of Literary Texts in Computational Literary Studies

从我们在SPP 2207“计算文学研究”(CLS)项目的第一阶段中的“文学作品中的关键段落”开始，我们探索了利用以解释性文本表达的文学研究中的专家知识的新方法。通过这种方式，我们为SPP的研究计划中规定的目标做出了贡献：将定性方法获得的研究成果与定量方法相结合。在第二阶段，我们扩展了这一方法，为重新利用现有的文学研究资源和结合研究实践开辟了更多的可能性，这在文学研究和CLS中是常见的。正如我们在对关键段落的分析中所表明的那样，我们已经经验地记录了专业口译员认为特别重要的段落，学术口译设定了加权的优先顺序或重点，而CLS通常以同样选择性和专注的方式进行。标准的定量聚类方法假设文学文本在其所有段落中都是同等重要的(在调查的优先方面，例如，最频繁的单词、情感等)。以对关键段落的分析为起点，我们现在想问一问，专家知识在多大程度上可以成为口译加权程序的关键，以及这种“熟练”的阅读如何能够推动CLS的方法。尽管关键段落仍然是本项目的部分焦点，但我们的目标是利用文学研究中的专家知识，从三个不同的方面改进CLS方法。焦点来自我们之前的研究，因此这些研究务实地互惠互利；每项研究都与现有的CLS专业知识相联系，从那里测试风险更高的研究选项。1.叙事结构检测：我们想问的是，事件的确定如何有助于理解关键段落，以及如何识别叙事学方面，特别是文学文本中的情节结构。2.情感分析：我们想要颠覆常用情感分析方法的视角，利用解释性文本中已经存在的关于情感的知识。最后但并非最不重要的一点是，我们想问一问，如何将基于文本的情绪检测方法与那些预设知识的方法结合起来，这些知识显然超出了情绪主题化的范畴。3.文本聚类：最后，我们以文学文本分组为例，转向复杂聚类。同时，我们想问一下，我们试探性地称为均匀(CLS)或加权选择性/聚焦(专家)的文本处理如何组合。总而言之，我们的总体研究问题是：如何在CLS的框架内以新的方式使用已经建立的解释性知识，以便最有效地利用现有的文学研究资源，并建立跨学科的联系？

Starting with “Key passages in literary works” in the first phase of our project within the SPP 2207 „Computational Literary Studies“ (CLS), we have explored new ways to draw on expert knowledge in literary studies, expressed in interpretative texts. In this way, we contribute to the goal stated in the research program of the SPP: to combine research results acquired with qualitative methods with quantitative methods. In this second phase, we extend this approach, to open up further possibilities for re-using existing resources of literary studies and for combining research practices, which are common in literary studies and in CLS. As we have shown in the analysis of key passages, where we have empirically recorded which passages professional interpreters consider particularly important, scholarly interpretations set weighted priorities or focuses, whereas CLS typically proceed in an equally selective and focusing manner. Standard quantitative clustering approaches assume that literary texts are evenly significant in all their passages (in the respect that was prioritized for the investigation, for example, most frequent words, emotions etc.). Taking the analysis of key passages as a starting point, we now want to ask to what extent expert knowledge can be key to these weighting procedures of interpretation, and how such ‘skillful’ readings can advance the approaches of CLS.While key passages remain partly in the focus of the project, we aim to leverage expert know-how in literary studies to improve CLS methods in three different ways. The foci have emerged from our previous research, so that the studies pragmatically mutually benefit from each other; each of them connects to existing CLS expertise to test riskier research options from there. 1. Narrative structure detection: We want to ask how the determination of events can contribute to the understanding of key passages and how narratological aspects, especially concerning plot structures in literary texts, can be identified. 2. Sentiment analysis: We want to reverse the perspective of common sentiment analysis approaches and leverage the already existing knowledge about emotions in interpretative texts. Last but not least, we want to ask how text-based approaches for emotion detection can be combined with those that presuppose knowledge that clearly goes beyond the emotions thematized. 3. Text clustering: Finally, we turn to complex clustering using the example of grouping literary texts into literary epochs. At the same time, we want to ask how what we have heuristically called evenly (CLS) or weighted selective/focused (experts) text processing can be combined. To sum up, our overall research question is: How can already established interpretative knowledge be used in new ways within the framework of CLS in order to make the most effective use of existing resources in literary studies and to make interdisciplinary connections available?