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

从SPP 2207“计算文学研究”（CLS）项目第一阶段的“文学作品中的关键段落”开始，我们探索了利用文学研究中的专家知识的新方法，这些知识在解释性文本中表达。通过这种方式，我们为SPP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结果与定量方法相结合。在第二阶段，我们扩展了这种方法，为重新利用文学研究的现有资源和结合文学研究和CLS中常见的研究实践开辟了进一步的可能性。正如我们在对关键段落的分析中所展示的那样，我们根据经验记录了专业口译员认为哪些段落特别重要，学术口译设定了加权优先级或重点，而CLS通常以同样选择性和重点的方式进行。标准的定量聚类方法假设文学文本在其所有段落中具有均匀的重要性（在调查优先考虑的方面，例如，最常见的单词，情感等）。以关键段落的分析为出发点，我们现在想问的是，在多大程度上专家知识可以成为这些解释加权程序的关键，以及这种“熟练”的阅读如何推进CLS的方法。虽然关键段落仍然是该项目的重点，但我们的目标是利用文学研究中的专家知识，以三种不同的方式改进CLS方法。这些焦点来自我们以前的研究，因此这些研究实际上相互受益;它们中的每一个都与现有的CLS专业知识相联系，以测试风险更高的研究选择。1.叙事结构检测：我们想问的是，事件的确定如何有助于对关键段落的理解，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.情绪分析：我们希望扭转常见的情感分析方法的角度，并利用现有的知识解释性文本中的情感。最后但并非最不重要的是，我们想问如何基于文本的情感检测方法可以与那些预先假设的知识，显然超出了主题化的情感相结合。3.文本聚类：最后，我们转向复杂聚类，使用将文学文本分组为文学时代的例子。与此同时，我们想知道如何将我们所称的均匀（CLS）或加权选择/集中（专家）文本处理结合起来。总之，我们的总体研究问题是：如何可以已经建立的解释性知识，以新的方式在CLS的框架内使用，以最有效地利用现有的资源在文学研究，并使跨学科的连接可用？

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?