从人文视角梳理文本关联的量化研究方法与应用
Quantitative Intertextuality from the Digital Humanities Perspective: A Survey
- 基于统计到深度学习的方法，系统分析多语言文本关联
- 覆盖跨语言、跨主题的大规模文本关系研究
- 适合对AI+人文学科交叉研究感兴趣的学者
文学理论中的文本间性指文本之间的关联，在数字人文研究中具有重要理论基础。过去十年，自然语言处理技术的发展使文本间性研究迈入量化时代，基于前沿方法的大规模研究不断涌现。本文为量化文本间性研究提供路线图，综述其数据、方法与应用。涵盖多语言、多主题数据，回顾从统计到深度学习的方法体系，并总结其在人文学科与社会科学中的应用及配套平台工具。随着计算机技术进步，更精确、多样且大规模的文本间性研究可期。文本间性有望在人工智能与人文学科交叉研究中发挥更大作用。
原文摘要 · Abstract (English)
The connection between texts is referred to as intertextuality in literary theory, which served as an important theoretical basis in many digital humanities studies. Over the past decade, advancements in natural language processing have ushered intertextuality studies into the quantitative age. Large-scale intertextuality research based on cutting-edge methods has continuously emerged. This paper provides a roadmap for quantitative intertextuality studies, summarizing their data, methods, and applications. Drawing on data from multiple languages and topics, this survey reviews methods from statistics to deep learning. It also summarizes their applications in humanities and social sciences research and the associated platform tools. Driven by advances in computer technology, more precise, diverse, and large-scale intertext studies can be anticipated. Intertextuality holds promise for broader application in interdisciplinary research bridging AI and the humanities.
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