Yuqi Chen
Hi, I’m Yuqi Chen.
I’m a PhD candidate at Peking University.
My research interests primarily lie in:
(1) using quantitative methods such as spatial analysis, time series analysis, and social network analysis to advance archaeological and historical research;
(2) using AI methods to understand human sentiment and the evolution of cultural psychology.
Please feel free to reach out if you have any questions or collaboration ideas.
news
| Sep 18, 2024 | I’m honored to be invited to deliver a talk for the “Frontiers in Chinese Studies” series organized by Professor Song Chen at Bucknell University. Title: Historical Minds and Artificial Intelligence: How AI Helps Rediscover Ancient Minds. Abstract: Advances in AI, particularly in natural language processing, have enabled large-scale quantitative analysis of historical texts. This talk presents two cases using AI models to explore intellectual debates and cultural psychology in premodern China.  |
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| Sep 18, 2024 | Our work has been proudly accepted by The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024). Title: [Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese](https://aclanthology.org/2024.emnlp-main.151.pdf).  |
| Jul 14, 2024 | I’m honored to present my work at The Tenth Annual Symposium on Quantitative History. Title: The Waves of Social Changes: A Quantitative Analysis of Ritual Bronzes in Early China.  |
| Jun 12, 2024 | I’m honored to be invited to deliver a talk for the “Fu Xuancong Academic Lecture” series organized by Professor Fan Xiang at Tsinghua University.  |
| Jun 03, 2024 | I’m honored to be invited to deliver a talk for the School of Chinese at The University of Hong Kong. Title: Surveying the Dead Minds: Historical Text Analysis Unveilling Psychological Dynamics Within Chinese Culture Across Two Millennia. Abstract: Humans have been creating texts for thousands of years, expressing their social norms, values, composing poetry, and storytelling within them. Historical texts contain rich yet underexplored sources of psychological data, condensing the thoughts, emotions, and behaviours of people who lived in the past. By utilizing the state-of-the-art natural language processing (NLP) methods, we developed a pipeline, called contextualized construct representations (CCR), to measure psychological constructs such as collectivism, individualism, norm strength, among others, in classical Chinese corpora. The large-scale historical text analysis unveils both temporal and spatial variations of various psychological constructs and moral values in pre-modern China, demonstrating the complex influences of a variety of factors, including climate, agriculture, conflicts, Confucianism, kinship, etc., on psychology and culture.  |
| Apr 02, 2024 | Our work using AI methods for psychometrics in historical texts has been proudly covered by MIT Technology Review China!  |