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- W2103841034 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." @default.
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- W2103841034 date "2012-12-03" @default.
- W2103841034 modified "2023-09-24" @default.
- W2103841034 title "How They Vote: Issue-Adjusted Models of Legislative Behavior" @default.
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