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
| May 20, 2025 | I’m honored to be invited to deliver a talk for the “Narratives and AI in Quantitative History: A Mandarin Workshop” organized by Professor Chicheng Ma at The University of Hong Kong. Title: AI for the Humanities. Abstract: The development of artificial intelligence is transforming the way humanities scholars conduct research. Adopting an interdisciplinary perspective, this presentation will illustrate how AI can be effectively integrated into the humanities and social sciences, including psychology, geography, and history, through three specific case studies: (1) surveying the dead minds; (2) geocoding the past world; and (3) renacting intellectual debates in history.  |
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| Apr 23, 2025 | Our work using LLMs for historical geography has been proudly published in International Journal of Geographical Information Science. Title: Geocoding the Past World: Unearthing Coordinates of Early China from Texts Using Generative AI. Abstract: Extracting geographic information from historical texts presents unique challenges. To address these challenges, this study leverages generative large language models (LLMs) to extract historical toponyms and their corresponding location references from texts. The coordinates of the extracted toponyms are then identified by a historical geocoder, which also calculates their maximum error distances based on the location references, indicating the degree of uncertainty. Both the extraction and geocoding processes are integrated into a novel tool named ‘His-Geo’ (https://github.com/yukiyuqichen/His-Geo). To evaluate the results, this study also curates a manually annotated dataset, the Early China Historical Geographic Corpus (CHGC-Early), filling the gap in the absence of geographic data for early China in existing gazetteers and providing a benchmark dataset for training and evaluating approaches for tasks related to geographic information extraction from premodern Chinese texts. The evaluation results show a satisfactory 0.831 F1 score for the GPT-4o model, demonstrating the remarkable capability of generative large language models in extracting geographic information from lengthy, unstructured texts that encompass diverse and sometimes conflicting views.  |
| 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.  |
| 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.  |
| 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.  |