nips nips2012 nips2012-154 nips2012-154-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Sean Gerrish, David M. Blei
Abstract: We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers’ positions on specific political issues. Our model can be used to explore how a lawmaker’s voting patterns deviate from what is expected and how that deviation depends on what is being voted on. We derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we demonstrate both improvement in heldout predictive performance and the model’s utility in interpreting an inherently multi-dimensional space. 1
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