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2023/10/19 【Oct. 20】Embedding Regression: Models for Context-Specific Description and Inference

Prof. Arthur Spirling, Princeton University
Date: Friday, October 20 - 09:00AM~12:00PM (Taiwan Time, GMT+8); Thursday, October 19 - 20:00PM~23:00PM (US Central Time, GMT-6).
Topic: Embedding Regression: Models for Context-Specific Description and Inference

Abstract: Social scientists commonly seek to make statements about how word use varies over circumstances— including time, partisan identity, or some other document-level covariate. For example, researchers might wish to know how Republicans and Democrats diverge in their understanding of the term “immigration.” Building on the success of pretrained language models, we introduce the à la carte on text (conText) embedding regression model for this purpose. This fast and simple method produces valid vector representations of how words are used—and thus what words “mean”—in different contexts. We show that it outperforms slower, more complicated alternatives and works well even with very few documents. The model also allows for hypothesis testing and statements about statistical significance. We demonstrate that it can be used for a broad range of important tasks, including understanding US polarization, historical legislative development, and sentiment detection. We provide open-source software for fitting the model.

Bio: Arthur Spirling is the Class of 1987 Professor of Politics. He received a bachelor's and master's degree from the London School of Economics, and a master's degree and PhD from the University of Rochester. Previously, he served on the faculties of Harvard University and New York University. Spirling's research centers on quantitative methods for analyzing political behavior, especially institutional development and the use of text-as-data. His work on these subjects has appeared in outlets such as the American Political Science Review, the American Journal of Political Science and the Journal of the American Statistical Association. Currently he is active on problems at the intersection of data science and social science, including those related to machine learning, and large language models. He previously won teaching and mentoring awards at Harvard University and NYU, along with the "Emerging Scholar" prize from the Society for Political Methodology.
Learn more about Prof. Arthur Spirling at his website.