09:00 | Thursday, June 16, 2022
Andrew E. Furer Professor of Economics, Harvard University
Vast amounts of data are trapped in non-computable formats, such as document image scans and text. Deep learning has the potential to greatly expand the questions that economists can study by providing rigorous methods for converting non-computable information into structured, computable data. Combined with advances in GPU compute and inexpensive cloud compute, this makes it feasible to process data on a massive scale.
In this Quantitative History Webinar, Melissa Dell of Harvard University provides an overview of her recent work to develop deep learning methods and tools for creating computable social science data, with an aim of making structured digital data more representative of documentary history. This work emphasizes lower resource contexts - for which there are few incentives for commercial technology – and encompasses novel approaches and tools for document layout analysis, OCR, and NLP pipelines.
Thursday, June 16, 2022
The Quantitative History Webinar Series aims to provide researchers, teachers, and students with an online intellectual platform to keep up to date with the latest research in the field, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The Series, now in its third year, is co-organized by the International Society for Quantitative History, HKU Business School, and Hong Kong Institute for the Humanities and Social Sciences. 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由國際量化歷史學會、香港大學經管學院和香港人文社會研究所全力支持和承辦。
Conveners: Professor Zhiwu Chen & Dr. Chicheng Ma (HKU Business School)
The International Society for Quantitative History (ISFQH) is an independent, not-for-profit organization dedicated to promoting, supporting, and enhancing the advancement of education, in particular research and knowledge dissemination in quantitative history, in Hong Kong and other parts of the world.