用自动生成的故事语法分析拉丁史诗叙事结构
Modeling Narrative Structure in Latin Epic Poetry with Automatically Generated Story Grammars
- 基于大模型和少样本学习自动生成故事元素标签
- 为古典文学研究提供可解释的叙事分析工具
- 适合人文学者与机器学习研究者协同使用
计算方法分析散文与诗歌常依赖词嵌入等抽象表示，可能忽略语境丰富的文学文本。受阅读心理启发，我们利用故事结构与叙事元素模拟人类叙事理解，构建更全面的文学文本表征。提出一种自动为输入文本生成故事语法标签的方法，兼具可解释性与可访问性，适用于人文学家与技术研究者。通过大语言模型流水线与少样本学习，对拉丁史诗进行故事元素标注，并直接用于叙事结构与风格分析。该方法帮助学者发现跨文本的新研究方向，也为下游机器学习任务提供新特征集。
原文摘要 · Abstract (English)
Computational methods for analyzing prose and poetry utilize word embeddings and other abstract representations that sometimes obscure context-rich literary text. Inspired by the psychology of reading, we utilize story structure and elements to simulate human narrative comprehension to produce a more comprehensive representation of literary text. We present a method for automatically generating story grammar labels for input texts as a means of analysis that is interpretable and accessible by humanists and technologists alike. Using a large language model (LLM) pipeline and few-shot learning, we label Latin epic poetry with story element labels and use this output directly to aid an analysis of the story structure and style. Our method guides literary scholars to discover new areas of interest across texts and provides a new feature set for further study for downstream machine learning tasks.
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