How a Bill Becomes a Law - Predicting Votes from Legislation Text
نویسنده
چکیده
Thanks to the efforts of organizations like GovTrack (Tauberer, 2012), a tremendous amount of roll-call and bill-text data has become available over the past few years. At the same time, most analyses are based on idealpoint models that use only the votes themselves in modelling voting decisions, and ignore bill text. We analyze a variety of models for predicting the outcomes of roll-call votes given vote text. We arrive at a neural network classifier which takes counts of words in bill texts as input and achieves state-of-theart accuracy in predicting vote outcomes.
منابع مشابه
An Embedding Model for Predicting Roll-Call Votes
We develop a novel embedding-based model for predicting legislative roll-call votes from bill text. The model introduces multidimensional ideal vectors for legislators as an alternative to single dimensional ideal point models for quantitatively analyzing roll-call data. These vectors are learned to correspond with pre-trained word embeddings which allows us to analyze which features in a bill ...
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