“遗产”主题文献阅读

N-HSSC | 2023 应用深度神经网络构建文献互文网络

发布日期:2024-06-02

文章来源:Siyu Duan, Jun Wang, Hao Yang, Qi Su. (2023). Disentangling the cultural evolution of ancient China: a digital humanities perspective. Humanities and Social Sciences Communications: 10, 310. https://doi.org/10.1057/s41599-023-01811-x.

整理人:徐嘉苗,2021级本科生

整理时间:2024年5月22日




Abstract: Being recognized among the cradles of human civilization, ancient China nurtured the longest continuous academic traditions and humanistic spirits, which continue to impact today’s society. With an unprecedented large-scale corpus spanning 3000 years, this paper presents a quantitative analysis of cultural evolution in ancient China. Millions of intertextual associations are identified and modelled with a hierarchical framework via deep neural network and graph computation, thus allowing us to answer three progressive questions quantitatively: (1) What is the interaction between individual scholars and philosophical schools? (2) What are the vicissitudes of schools in ancient Chinese history? (3) How did ancient China develop a cross-cultural exchange with an externally introduced religion such as Buddhism? The results suggest that the proposed hierarchical framework for intertextuality modelling can provide sound suggestions for large-scale quantitative studies of ancient literature. An online platform is developed for custom data analysis within this corpus, which encourages researchers and enthusiasts to gain insight into this work. This interdisciplinary study inspires the re-understanding of ancient Chinese culture from a digital humanities perspective and prompts the collaboration between humanities and computer science.

摘要:作为人类文明的摇篮之一,中国古代孕育了延续最久的学术传统和人文精神,并继续影响着今天的社会。本文利用跨度达3000年的空前大规模语料库,对中国古代的文化演变进行了定量分析。数以百万计的互文关联被识别出来,并通过深度神经网络和图计算用层次框架建模,从而使我们能够定量地回答三个渐进的问题: (1)个别学者和哲学流派之间的相互作用是什么? (2)中国古代史上各学派的变迁是怎样的? (3)古代中国是如何与外来传入的宗教如佛教进行跨文化交流的?结果表明,本文提出的互文性层次化模型可为古代文献的大规模定量研究提供有益的建议。在这个语料库中开发了一个用于自定义数据分析的在线平台,鼓励研究人员和爱好者深入了解这项工作。这一跨学科研究激发了数字人文视角下对中国古代文化的重新认识,促进了人文科学与计算机科学的合作。

图 几部经典的五派倾向指数

图 历史上五所学校的历史地位指数



原文链接:https://www.nature.com/articles/s41599-023-01811-x 

节选转引:https://mp.weixin.qq.com/s/aNTMcF6KWu9EsQhsNAGZKA 

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