Computational recognition of narratives, if successful, would find innumerable applications with large digitized datasets. Systematic identification of narratives in the text flow could significantly contribute to such pivotal questions as where, when, and how narratives are employed. This paper discusses an approach to extract narratives from two datasets, Finnish parliamentary records (1980–2021) and oral history interviews with former Finnish MPs (1988–2018). Our study was based on an iterative approach, proceeding from original expert readings to a rule-based, computational approach that was elaborated with the help of annotated samples and annotation scheme. Annotated samples and computationally found extracts were compared, and a good correspondence was found. In this paper, we exhibit and compare the results from annotation and rule-based approach, and discuss examples of correctly and incorrectly found narrative sections. We consider that all attempts at recognizing and extracting narratives are definition dependent, and feed back to narrative theory.

中文翻译：

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叙事的计算识别


叙述的计算识别如果成功，将在大型数字化数据集中找到无数的应用。在文本流中系统地识别叙述可以极大地促进诸如在何处、何时以及如何使用叙述等关键问题。本文讨论了一种从芬兰议会记录（1980-2021 年）和对芬兰前议员的口述历史访谈（1988-2018 年）两个数据集中提取叙述的方法。我们的研究基于迭代方法，从原始专家阅读到基于规则的计算方法，该方法在注释样本和注释方案的帮助下进行了详细说明。将注释样本和计算发现的提取物进行比较，发现具有良好的对应关系。在本文中，我们展示和比较了注释和基于规则的方法的结果，并讨论了正确和错误找到的叙述部分的示例。我们认为，所有识别和提取叙事的尝试都依赖于定义，并反馈给叙事理论。